What did the scientific literature learn from internal company documents in the pharmaceutical industry? A scoping review
Bibliographic record
Abstract
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.
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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 | 0.060 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.065 | 0.047 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".