Davids versus Goliaths: Pharma and academia threats to individual scientists and clinicians
Bibliographic record
Abstract
Background We previously described experiences of clinicians who published adverse drug reaction reports. We now report on threats and intimidations leveled against clinicians and scientists who received publicly documented threats after communicating safety, efficacy, or data integrity findings contrary to corporate interests. Methods Data on threats and intimidations were obtained from transcripts of governmental hearings or agencies, university-affiliated reports, media interviews, and investigative journalism articles. Content and timing of threats and intimidation, subsequent harms, numbers of persons seriously injured or who died from individual toxicities, financial payments from sponsors related to safety, efficacy, or data integrity concerns, and civil settlements and criminal findings were evaluated. Findings Twenty-six individuals who communicated safety, efficacy, or data integrity concerns were targets of threats and intimidation from corporate employees (twenty-three individuals) or regulatory personnel (three). Seventeen individuals identified instances where pharmaceutical sponsors submitted fraudulent data in support of regulatory approval of a drug or device. Scientist and clinician communications were followed by drug/device withdrawals (fourteen drugs/devices), black box warnings (six drugs), withdrawal of a sponsor’s application for regulatory approval (one device), and delay of approval of a sponsor’s application for regulatory approval (one drug). Actions mainly occurred after persons communicated with pharmaceutical employees (fourteen). Intimidation efforts by corporate personnel included threats of lawsuits (eighteen individuals), hiring private investigators (nine), and public disparagement at conferences (eleven). Related intimidation efforts carried out by academia or regulatory agency superiors included threats of: loss of positions (six), loss of grant funding (two), delays in decisions regarding tenure (two); or reassignment to a low-level position (one). Academic harms included lost: hospital or university appointments (nine and six, respectively), grant funding (two), chairperson title of an international clinical trial group (one), and journal editorial board position (one). Corporate harms included payment of $1 million to defense attorneys in three cases filed against clinicians. Interpretation Threats and intimidation carried out by corporate employees and/or academic supervisors followed public communication of concerns regarding patient safety, drug efficacy, or data integrity, including instances where sponsors were identified as having submitted fraudulent data to regulatory or government agencies. Consideration should be given to filing criminal charges against pharmaceutical executives who are discovered by scientists or clinicians to have knowingly submitted fraudulent data to regulatory or governmental agencies, rather than causing the scientists and clinicians who submit such reports to risk losing their reputations and occupations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.038 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".