excellent work, thank you for this article. the weird subject IDs were one of the first things that irked me when i started looking at the files, the first C459 patient is 10011004.
unless i missed something, one explanation could be that these were trial participants who received BNT162a1 or BNT162c2, as all patient level data for these vax candidates was removed from the documents submitted to the FDA. this might also explain the discrepancy between the clinicaltrials.gov C459 start date of 29th april in contrast to the first recorded dose on may 4th.
but the numbers dont match up. page 25/286 of the 1st PSUR (https://tkp.at/wp-content/uploads/2023/01/1.PSUR_orginial.pdf) lists amount of patients exposed to each vaccine candidate: a1+c2 only amount to 126 patients. if you include b2s01, you get 456 patients; adding b3 gives 222, so that more or less sinks that hypothesis and only makes the discrepancies harder to explain, and more likely to be nefarious.
hats off to another impeccably researched and written article!
But they were properly tagged as "screen failure" or "not assigned" (randomized), which explains they don't appear in the .PDF exports. As far as these 301 "simply disappeared" are concerned, it's another - way more concerning symptom - for the motives detailed.
thanks! this makes your discovery all the more explosive. the possibility of there being a good reason for the deleted subject ids is close to zero for me now.
Here's a company that has performed or has contacted others to perform 100's if not 1,000's of drug trials. Still they screw up this bad? Then again they often x-out negative trial participants and results to get the final results they need.
Furthermore, these mRNA trials were a total scam and since they were contracted under EUA by the DOD, there was no requirement that they be accurate or completed. We were going to get mRNA injections into the public domain no matter what the trials showed...even if a million people died during their processing.
I glanced through the Twitter feed, and while I didn't fully read this article, it looks like a job well done. I'll have to come back later and give it the attention it deserves.
We already answered a few of these questions in past papers ; but a more detailed reproduction of the efficacy analysis is in the tubes. We will release it as soon as Josh's & my own sense of perfectionism are satisfied - which can take some time given the mess they left us with & our shared desire to only put accurate data out-there 🤨
Here is some food for thought, in case it might help inform any efficacy reanalysis you might think up:
1) It seems to me the outcome we actually ought to have cared about is severity of covid, *given covid*. This is because everyone is gonna get covid in any case. By my calculations, the original trial would have said 53% efficacy by this measure. Calculations for that:
there was 1 severe case out of 8 cases in the vaccine group. In the placebo group, there were 9 severe cases out of 162 cases. That is 5.6% of cases being severe versus 12.5% of cases being severe. With no confidence in those estimates. That is only 55% protection against severe cases, by this measure.
- 82 vaccinated cases more than 7 days after second dose
- 889 unvaccinated cases more than 7 days after second dose.
- 1 severe vaccinated case more than 7 days after second dose
- 23 severe unvaccinated cases more than 7 days after second dose.
Doing same calculations, efficacy against a given case being severe is:
After first dose: = [1 - (1/131)/(30/1034)] = 74% efficacy
More than 7 days after second dose: [1 - (1/82)/(23/889)] = 53% efficacy.
Again with such small sample sizes, no confidence. In short, the trials don’t tell us what actually would have mattered.
2) You may already be aware of this. Pfizer vaccine trial found that vaccination had only 53% efficacy against cases, if you defined it as anti-nucleocapsid seroconversion.
Then we must account for the fact that we know from elsewhere that vaccines seems to lead to reduced N-ab seroconversion. So by this measure, vaccine efficacy against cases is even less than 53%! But, in defense of vaccines, we really shouldn’t care about that outcome in this context. Nor should we care about PCR confirmed cases. We should only really care about reduction in case severity.
the problem here is the required assumption of equal treatment between placebo and vaccine arms by the trial sponsor(i.e. double-blinded), which was fulminantly proven false in a previous article by openvaet and mr guetzkow https://openvaet.substack.com/p/pfizerbiontech-c4591001-trial-local
Excellent work! It takes a lot of time and energy to work through these details. Thank you!
The basis for approving (and then mandating) the vaccine was the teported values of 162 placebo vs 8 BNT-Pfizer recipients getting Covid.
Clearly, with hundreds of billions of dollars at stake, there would have been a motive to find an excuse to exclude ~154 trial participants who received BNT-Pfizer doses and then got Covid during the study period. I’m not saying that necessarily happened, but the motive is obvious.
The motive for covering up more severe adverse effects of the vaccine (more than “just a mistake”) is even clearer.
Superb analysis, thank you. Could you or someone please shed light on this please...? Did I miss something?
I noted the following apparent discrepancy back in Dec 2020 with the initial NEJM Pfizer study that Adverse Events Table S3. (Appendix) | Participants Reporting at Least 1 Adverse Event from Dose 1 (All Enrolled Participants). BNT162b2 (30μg) (n=21621), Placebo (n=21631). [N=43252]
While, pp 2608 of the primary article states:
“Adverse event analyses are provided for ALL enrolled 43,252 participants, with variable follow-up time after dose 1 (Table S3 ~ Appendix). More BNT162b2 recipients than placebo recipients reported any adverse event (27% and 12%, respectively) or a related adverse event (21% and 5%).”
However, in the (Abstract) Results, it is stated that 21,720 received BNT162b2 and 21,728 received placebo (n=43,448).
Table S3 (Appendix) therefore does not appear to include 196 participants?
Thanks for your feedback. There are countless discrepancies in the documents, some more serious than others.
196 subjects rings a first bell as far as the fact that phase 1 was 195 subjects.
One population could be including phase 1 participants - and the gap could be a non-accounted withdrawal or other bug.
I would refer to the ADRG to look for the precise population definition. But in the end given that they have messed up with a lot of the subjects they did kept (10801222, 11201436, 11491100, 11491108, 12511204 for example should be excluded for not receiving a first dose but aren't documented properly in November and spawning only on the BLA documentation) tracing back their figures is a long exercise.
I personally have better knowledge of the efficacy population than the safety one, at this stage, sadly, so aside for these few leads, can't answer you precisely.
Thank you. It is appalling that an NEJM reviewer did not appear to pick this kind of discrepancy up. If table totals, article totals and abstract totals don't match there's a signal of concern in that alone, particularly if nigh on 200 disappear from the adverse reactions table. Did they die? Who knows. Pfizer has infinite resources and a superb range of personnel to generate the required product. Astonishing. One might only speculate that the journal of 'pregnant people' was beside itself to publish.
Assuming that the data is not all held in one table. If it is then either the insert is successful and the new ID is allocated by the DB, or its not, and no ID is allocated.
However, it is often the case that the data you enter is spread across several tables. For example, they might create a "subject" data row in the "subjects" table and at the same time insert some additional data into a another table that hold records about interactions with the subject. It could easily be the case that data is entered into several tables as a result of one form submission.
That being the case, if you are a good developer, you'd wrap all the data inserts into a "transaction". If all your inserts for this submission succeed then you "confirm" the transaction and that becomes the new state of the database.
On the other hand, if any of your inserts - maybe the last one in some supplemental table - fails, then you "rollback" the transaction and the DB removed everything that might have been inserted (or updated / deleted) as part of that transaction. Expect that sequential IDs are lost.
The reason for this is that DBs can process many requests at the same time. So you might do an insert and generate ID 100, and whilst all your inserts are taking place, someone else starts a transaction and they get ID 101. If the DB rolled back the sequence so that the next person gets 100, then after that someone else would get 101 - which has already been used.
On something important like this, personally, I'd track any failed transaction and note the ID and reason for failure. That way you have an audit trail of when these occur and why - very important if you want to fix the system to prevent future failures - but also important if you want to show that numbers are missing as a result of a rollback rather than for some other reason. The table you use for this shock also have auto-number sequences so that you can see no entries in there have been removed...
However, sadly, few developers are that careful about their work and I doubt very much such safeguards have been put in place. This sort of thing is usually more common in system that handle money. People are a lot more carefully about accounting for money that people...
"However, it is often the case that the data you enter is spread across several tables. For example, they might create a "subject" data row in the "subjects" table and at the same time insert some additional data into a another table that hold records about interactions with the subject. It could easily be the case that data is entered into several tables as a result of one form submission."
Are we assuming that Firecrest, the software used to handle the trial, bought in 2011 by ICON, is being developed "during" the trial here?
There were supplementary data stored in "dedicated" tables. None were supposed to be used at subject creation stage - which requires only a limited quantity of mandatory 1 to 1 data upon pre-screening (subject's age, race, name, and basic demographic data). This is further shown by more than 1k entries which have then failed to pass screening and for whom no further data was entered.
You don't enter trial-specific details which could fail - for example a new local PCR test, on the same transaction that you use to initiate a subject.
There should indeed be some audit trail of said deletions. If not, said changes will be tracked in the database binary logs, unless they have been disabled.
Aside for the wrong assumption that we need explanations on transaction commits, your approach fails to explain why the phenomenon is observed on such a limited number of sites.
To conclude, thanks to keep your argumentation to this thread and to avoid to declare me wrong on other sub-threads without further arguments, that behavior while leaving me no time to answer will shorten the conversation.
So, your 3 out-of-topic comments without replying to this one are here as I don't like to "censor" critics: https://ibb.co/GMk6Hq9
Meanwhile I also like to keep threads readable, so I'll flush them and thank you to use the "Reply" button on this comment prior to get banned for a week.
As a computer programmer I find it hard to even read this article. Let me just point out a little code - in Oracle, for example, (the largest database software company in the world and likely used for the systems above ) the standard way to generate and assign ID's to a table (an object that holds data) is using a sequence. Here is an example of the code the defines a sequence:
CREATE SEQUENCE TMPL_TEMPLATES_SEQ
START WITH 10000
INCREMENT BY 1
CACHE 20
NOCYCLE;
Note the CACHE 20 - this tells the system to preallocate 20 values in memory - usually for performance reasons. Now if a user logons onto the system and creates a user session and calls the sequence (when creating a row of data) the database will store (cache) 20 values for use in memory. When the use inserts say two rows of data the ID values will come from memory. If the user logs off and logs on again the sequence will reset and will start with the next batch - ie PLUS 20. IF he or she creates another row :
Then the values in the table for the ID will be
10000
10001
10020
etc
I am putting this here to explicitly say - DO NOT MAKE ASSUMPTIONS ABOUT COMPUTER PROGRAMS WITHOUT SEEING THE CODE.
Having said that - I am absolutely no fan of big pharma and the governments that have allowed them to perpetrate (IMO) one of the greatest scandals / crimes of recent times.
But please be careful about how you research this.
How do your 301 missing cases relate to the 54 / 45 subjects "randomised but not exposed" (CSR FInal Report, Table 14.258)?
How to your 301 missing cases relate to the 97 / 90 subjects missing in the "Safety Population" (cf. CSR, Table 8; 21621 / 21631) from the "Vaccinated Dose 1" (21718 / 21731; note that few, about 14, were excluded from the Safety Population due to a dose that was not the ranomised one).
Thanks. The missing cases don't exist as far as any report or analysis of the data is concerned. Exclusions from analysis are not the same, because those exclusions are recorded. The missing cases are like ghosts, whose existence is inferred based on gaps in the subject numbering sequence.
Hi, it's not FOI. It was ICAN successful legal action pre the trials? So public. USA only as that's where Del Bigtree is. It's one of their issues with all vax over many years. It'll be on their website and I'm sure they'd be helpful and interested in analysis? He did programs on it way back in 2020. I'll have a look see as well.
I haven't seen anything about this in all the Cvax comparisons and if Pharma lumped all the country trials together? I vaguely recall they also used different placebos in the different countries too? And different per company? So saline in USA plus plus how many other 'traditional' vax in the other countries? It's never mentioned but it must be in the pre trial paperwork per country?
excellent work, thank you for this article. the weird subject IDs were one of the first things that irked me when i started looking at the files, the first C459 patient is 10011004.
unless i missed something, one explanation could be that these were trial participants who received BNT162a1 or BNT162c2, as all patient level data for these vax candidates was removed from the documents submitted to the FDA. this might also explain the discrepancy between the clinicaltrials.gov C459 start date of 29th april in contrast to the first recorded dose on may 4th.
but the numbers dont match up. page 25/286 of the 1st PSUR (https://tkp.at/wp-content/uploads/2023/01/1.PSUR_orginial.pdf) lists amount of patients exposed to each vaccine candidate: a1+c2 only amount to 126 patients. if you include b2s01, you get 456 patients; adding b3 gives 222, so that more or less sinks that hypothesis and only makes the discrepancies harder to explain, and more likely to be nefarious.
hats off to another impeccably researched and written article!
Thanks !
In that specific case the subjects do exist 10011001, 10011002.. in the database (cf the attached files with all the available ids : https://docs.google.com/spreadsheets/d/1T3sPWhA67nX9lFerFaEiaMusKEG8qnG0/edit#gid=2020743872 ).
But they were properly tagged as "screen failure" or "not assigned" (randomized), which explains they don't appear in the .PDF exports. As far as these 301 "simply disappeared" are concerned, it's another - way more concerning symptom - for the motives detailed.
thanks! this makes your discovery all the more explosive. the possibility of there being a good reason for the deleted subject ids is close to zero for me now.
Zero surprise. The corruption is even bigger than we imagine, and we imagine the worst has transpired.
Speaking of unpublished ID#'s there is about 30,000 unpublished ID# in VAERS during the covid19 jab ERA. https://www.vaersaware.com/unpublished-reports-id-s
https://imgur.com/gallery/s434VF0
Here's a company that has performed or has contacted others to perform 100's if not 1,000's of drug trials. Still they screw up this bad? Then again they often x-out negative trial participants and results to get the final results they need.
Furthermore, these mRNA trials were a total scam and since they were contracted under EUA by the DOD, there was no requirement that they be accurate or completed. We were going to get mRNA injections into the public domain no matter what the trials showed...even if a million people died during their processing.
Wow.
I glanced through the Twitter feed, and while I didn't fully read this article, it looks like a job well done. I'll have to come back later and give it the attention it deserves.
Thanks.
Thank you for your work. The true death count may never be known. What has happened is so awful.
Great expose` you guys!
What I want to know is the ratio of the following two numbers:
[Number who were lost to follow up] / [Number of symptomatic, positive PCR tests prevented]
What truly is the complete number of subjects lost to follow-up after initial screening?
Thanks for your feedback !
We already answered a few of these questions in past papers ; but a more detailed reproduction of the efficacy analysis is in the tubes. We will release it as soon as Josh's & my own sense of perfectionism are satisfied - which can take some time given the mess they left us with & our shared desire to only put accurate data out-there 🤨
Here is some food for thought, in case it might help inform any efficacy reanalysis you might think up:
1) It seems to me the outcome we actually ought to have cared about is severity of covid, *given covid*. This is because everyone is gonna get covid in any case. By my calculations, the original trial would have said 53% efficacy by this measure. Calculations for that:
In the original Pfizer trial,
https://www.nejm.org/doi/full/10.1056/nejmoa2034577
there was 1 severe case out of 8 cases in the vaccine group. In the placebo group, there were 9 severe cases out of 162 cases. That is 5.6% of cases being severe versus 12.5% of cases being severe. With no confidence in those estimates. That is only 55% protection against severe cases, by this measure.
In the 6-month follow up:
https://www.nejm.org/doi/full/10.1056/NEJMoa2110345
- 131 vaccinated cases after 1st dose
- 1034 unvaccinated cases after 1st dose
- 1 severe vaccinated case after 1st dose
- 30 severe unvaccinated cases after 1st dose
- 82 vaccinated cases more than 7 days after second dose
- 889 unvaccinated cases more than 7 days after second dose.
- 1 severe vaccinated case more than 7 days after second dose
- 23 severe unvaccinated cases more than 7 days after second dose.
Doing same calculations, efficacy against a given case being severe is:
After first dose: = [1 - (1/131)/(30/1034)] = 74% efficacy
More than 7 days after second dose: [1 - (1/82)/(23/889)] = 53% efficacy.
Again with such small sample sizes, no confidence. In short, the trials don’t tell us what actually would have mattered.
2) You may already be aware of this. Pfizer vaccine trial found that vaccination had only 53% efficacy against cases, if you defined it as anti-nucleocapsid seroconversion.
https://philharper.substack.com/p/a-public-verification-of-jikkyleaks
Then we must account for the fact that we know from elsewhere that vaccines seems to lead to reduced N-ab seroconversion. So by this measure, vaccine efficacy against cases is even less than 53%! But, in defense of vaccines, we really shouldn’t care about that outcome in this context. Nor should we care about PCR confirmed cases. We should only really care about reduction in case severity.
the problem here is the required assumption of equal treatment between placebo and vaccine arms by the trial sponsor(i.e. double-blinded), which was fulminantly proven false in a previous article by openvaet and mr guetzkow https://openvaet.substack.com/p/pfizerbiontech-c4591001-trial-local
Well, true, that false assumption throws off any and all analyses.
Excellent work! It takes a lot of time and energy to work through these details. Thank you!
The basis for approving (and then mandating) the vaccine was the teported values of 162 placebo vs 8 BNT-Pfizer recipients getting Covid.
Clearly, with hundreds of billions of dollars at stake, there would have been a motive to find an excuse to exclude ~154 trial participants who received BNT-Pfizer doses and then got Covid during the study period. I’m not saying that necessarily happened, but the motive is obvious.
The motive for covering up more severe adverse effects of the vaccine (more than “just a mistake”) is even clearer.
Can we use these revelations to litigate? In just seems like no regulators are willing to do their jobs? Maybe the judicial system?
Interesting to say the least.
Very thorough investigation, kudos!
Superb analysis, thank you. Could you or someone please shed light on this please...? Did I miss something?
I noted the following apparent discrepancy back in Dec 2020 with the initial NEJM Pfizer study that Adverse Events Table S3. (Appendix) | Participants Reporting at Least 1 Adverse Event from Dose 1 (All Enrolled Participants). BNT162b2 (30μg) (n=21621), Placebo (n=21631). [N=43252]
While, pp 2608 of the primary article states:
“Adverse event analyses are provided for ALL enrolled 43,252 participants, with variable follow-up time after dose 1 (Table S3 ~ Appendix). More BNT162b2 recipients than placebo recipients reported any adverse event (27% and 12%, respectively) or a related adverse event (21% and 5%).”
However, in the (Abstract) Results, it is stated that 21,720 received BNT162b2 and 21,728 received placebo (n=43,448).
Table S3 (Appendix) therefore does not appear to include 196 participants?
Thanks for your feedback. There are countless discrepancies in the documents, some more serious than others.
196 subjects rings a first bell as far as the fact that phase 1 was 195 subjects.
One population could be including phase 1 participants - and the gap could be a non-accounted withdrawal or other bug.
I would refer to the ADRG to look for the precise population definition. But in the end given that they have messed up with a lot of the subjects they did kept (10801222, 11201436, 11491100, 11491108, 12511204 for example should be excluded for not receiving a first dose but aren't documented properly in November and spawning only on the BLA documentation) tracing back their figures is a long exercise.
I personally have better knowledge of the efficacy population than the safety one, at this stage, sadly, so aside for these few leads, can't answer you precisely.
Thank you. It is appalling that an NEJM reviewer did not appear to pick this kind of discrepancy up. If table totals, article totals and abstract totals don't match there's a signal of concern in that alone, particularly if nigh on 200 disappear from the adverse reactions table. Did they die? Who knows. Pfizer has infinite resources and a superb range of personnel to generate the required product. Astonishing. One might only speculate that the journal of 'pregnant people' was beside itself to publish.
Very suspicious indeed!
Hi Josh,
Yeah, that is exactly the process.
Assuming that the data is not all held in one table. If it is then either the insert is successful and the new ID is allocated by the DB, or its not, and no ID is allocated.
However, it is often the case that the data you enter is spread across several tables. For example, they might create a "subject" data row in the "subjects" table and at the same time insert some additional data into a another table that hold records about interactions with the subject. It could easily be the case that data is entered into several tables as a result of one form submission.
That being the case, if you are a good developer, you'd wrap all the data inserts into a "transaction". If all your inserts for this submission succeed then you "confirm" the transaction and that becomes the new state of the database.
On the other hand, if any of your inserts - maybe the last one in some supplemental table - fails, then you "rollback" the transaction and the DB removed everything that might have been inserted (or updated / deleted) as part of that transaction. Expect that sequential IDs are lost.
The reason for this is that DBs can process many requests at the same time. So you might do an insert and generate ID 100, and whilst all your inserts are taking place, someone else starts a transaction and they get ID 101. If the DB rolled back the sequence so that the next person gets 100, then after that someone else would get 101 - which has already been used.
On something important like this, personally, I'd track any failed transaction and note the ID and reason for failure. That way you have an audit trail of when these occur and why - very important if you want to fix the system to prevent future failures - but also important if you want to show that numbers are missing as a result of a rollback rather than for some other reason. The table you use for this shock also have auto-number sequences so that you can see no entries in there have been removed...
However, sadly, few developers are that careful about their work and I doubt very much such safeguards have been put in place. This sort of thing is usually more common in system that handle money. People are a lot more carefully about accounting for money that people...
"However, it is often the case that the data you enter is spread across several tables. For example, they might create a "subject" data row in the "subjects" table and at the same time insert some additional data into a another table that hold records about interactions with the subject. It could easily be the case that data is entered into several tables as a result of one form submission."
Are we assuming that Firecrest, the software used to handle the trial, bought in 2011 by ICON, is being developed "during" the trial here?
There were supplementary data stored in "dedicated" tables. None were supposed to be used at subject creation stage - which requires only a limited quantity of mandatory 1 to 1 data upon pre-screening (subject's age, race, name, and basic demographic data). This is further shown by more than 1k entries which have then failed to pass screening and for whom no further data was entered.
You don't enter trial-specific details which could fail - for example a new local PCR test, on the same transaction that you use to initiate a subject.
There should indeed be some audit trail of said deletions. If not, said changes will be tracked in the database binary logs, unless they have been disabled.
Aside for the wrong assumption that we need explanations on transaction commits, your approach fails to explain why the phenomenon is observed on such a limited number of sites.
To conclude, thanks to keep your argumentation to this thread and to avoid to declare me wrong on other sub-threads without further arguments, that behavior while leaving me no time to answer will shorten the conversation.
So, your 3 out-of-topic comments without replying to this one are here as I don't like to "censor" critics: https://ibb.co/GMk6Hq9
Meanwhile I also like to keep threads readable, so I'll flush them and thank you to use the "Reply" button on this comment prior to get banned for a week.
As a computer programmer I find it hard to even read this article. Let me just point out a little code - in Oracle, for example, (the largest database software company in the world and likely used for the systems above ) the standard way to generate and assign ID's to a table (an object that holds data) is using a sequence. Here is an example of the code the defines a sequence:
CREATE SEQUENCE TMPL_TEMPLATES_SEQ
START WITH 10000
INCREMENT BY 1
CACHE 20
NOCYCLE;
Note the CACHE 20 - this tells the system to preallocate 20 values in memory - usually for performance reasons. Now if a user logons onto the system and creates a user session and calls the sequence (when creating a row of data) the database will store (cache) 20 values for use in memory. When the use inserts say two rows of data the ID values will come from memory. If the user logs off and logs on again the sequence will reset and will start with the next batch - ie PLUS 20. IF he or she creates another row :
Then the values in the table for the ID will be
10000
10001
10020
etc
I am putting this here to explicitly say - DO NOT MAKE ASSUMPTIONS ABOUT COMPUTER PROGRAMS WITHOUT SEEING THE CODE.
Having said that - I am absolutely no fan of big pharma and the governments that have allowed them to perpetrate (IMO) one of the greatest scandals / crimes of recent times.
But please be careful about how you research this.
Obviously you found so hard to read the article that you haven't.
1. We did verified the code used to generate these ids.
2. The behavior you theorize doesn't apply anywhere close to the symptoms described above.
Thank you very much, gerat job!
How do your 301 missing cases relate to the 54 / 45 subjects "randomised but not exposed" (CSR FInal Report, Table 14.258)?
How to your 301 missing cases relate to the 97 / 90 subjects missing in the "Safety Population" (cf. CSR, Table 8; 21621 / 21631) from the "Vaccinated Dose 1" (21718 / 21731; note that few, about 14, were excluded from the Safety Population due to a dose that was not the ranomised one).
Thanks. The missing cases don't exist as far as any report or analysis of the data is concerned. Exclusions from analysis are not the same, because those exclusions are recorded. The missing cases are like ghosts, whose existence is inferred based on gaps in the subject numbering sequence.
Hi, it's not FOI. It was ICAN successful legal action pre the trials? So public. USA only as that's where Del Bigtree is. It's one of their issues with all vax over many years. It'll be on their website and I'm sure they'd be helpful and interested in analysis? He did programs on it way back in 2020. I'll have a look see as well.
I haven't seen anything about this in all the Cvax comparisons and if Pharma lumped all the country trials together? I vaguely recall they also used different placebos in the different countries too? And different per company? So saline in USA plus plus how many other 'traditional' vax in the other countries? It's never mentioned but it must be in the pre trial paperwork per country?
Hence my question to you. Thanks for replying.
I'm sorry but despite several readings I don't understand your message. Don't hesitate to proceed in your native language and I'll translate.