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Record W4367181630 · doi:10.1002/cesm.12011

What did the scientific literature learn from internal company documents in the pharmaceutical industry? A scoping review

2023· review· en· W4367181630 on OpenAlexafffund
Marc‐André Gagnon, Blue Miaoran Dong

Bibliographic record

VenueCochrane Evidence Synthesis and Methods · 2023
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSnowball samplingScopusNonprobability samplingBusinessKnowledge managementComputer sciencePublic relationsPolitical scienceMEDLINESociologyMedicine

Abstract

fetched live from OpenAlex

Abstract Objective To identify all scientific papers that used internal industry documents in the pharmaceutical sector and analyze what and how the scientific literature learned about corporate influence in the pharmaceutical sector through these internal documents. Design Scoping review. Methods Using different series of keywords, we searched six databases, PubMed, Scopus, Web of Science, CINAHL, Business Source Complete, and PAIS, for peer‐reviewed journal articles analyzing pharmaceutical corporations' internal documents. We completed the scoping review using a purposive snowball sampling method to extract relevant case studies and peer‐reviewed journal articles from relevant articles' reference lists when our search keywords failed to capture them. To analyze the content of the literature and better categorize the types of corporate strategies at play in the pharmaceutical sector, we used categories of ghost‐management previously developed in the literature. Results We identified 37 peer‐reviewed papers in the final results. All the articles included in the final results are published in English. Almost all articles obtained most of their internal document data through legal proceedings. All 37 articles unveil dynamic ghost‐management strategies that pharmaceutical corporations employ to safeguard their corporate interest. The strategies identified relate to scientific capture ( n = 28), professional capture ( n = 16), regulatory capture ( n = 6), media capture ( n = 3), market capture ( n = 4), technological capture ( n = 2), civil society capture ( n = 4), and others ( n = 2). Conclusion The scientific literature using internal documents confirmed widespread corporate influence in the pharmaceutical sector. While the academic literature used internal documents related to only a handful of products, our research results, based on ghost‐management categories, demonstrate the extent of corporate influence in every interstice of pharmaceutical markets, particularly in clinical research and clinical practice. It also allows us to better refine the conceptual categories of ghost‐management to better map corporate influence and conflict of interest.

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)0.029
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.013
Insufficient payload (model declined to judge)0.0020.000

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 head0.644
GPT teacher head0.683
Teacher spread0.039 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations9
Published2023
Admission routes2
Has abstractyes

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