Davids versus Goliaths: Pharma and academia threats to individual scientists and clinicians
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,047 | 0,153 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,024 | 0,038 |
| Communication savante | 0,016 | 0,013 |
| Science ouverte | 0,002 | 0,023 |
| Intégrité de la recherche | 0,007 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».