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Record W4395669529 · doi:10.3389/fmed.2024.1327939

Promoting opioids, a story about how to influence medical science and opinions

2024· article· en· W4395669529 on OpenAlexafffund
Maud Bernisson, Sergio Sismondo

Bibliographic record

VenueFrontiers in Medicine · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

Key origins of the opioid crisis in the US lie in some pharmaceutical companies' substantial efforts to sell prescription painkillers. To legitimize opioids, the companies built up a body of medical science and opinions, and channels with which to communicate. Archival searches found 876 contracts that together provide information on how Mallinckrodt, an opioid manufacturer, attempted the ghost-management of medicine. These records-available because of litigation-involved contract research organizations, medical education and communication companies, publishers, professional societies, researchers, and other people who could be Mallinckrodt's agents. Together, they produced and circulated scientific messages to increase physicians' comfort with prescribing opioids. This article gives an overview of that activity, as seen in the contracts and related documents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.215
GPT teacher head0.532
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes2
Has abstractyes

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