Key Opinion Leaders and the Corruption of Medical Knowledge: What the Sunshine Act Will and Won’t Cast Light on
Bibliographic record
Abstract
The pharmaceutical industry, in its marketing efforts, often turns to "key opinion leaders" or "KOLs" to disseminate scientific information. Drawing on the author's fieldwork, this article documents and examines the use of KOLs in pharmaceutical companies' marketing efforts. Partly due to the use of KOLs, a small number of companies with well-defined and narrow interests have inordinate influence over how medical knowledge is produced, circulated, and consumed. The issue here, as in many other cases of institutional corruption, is that a few actors have accumulated the power to shape the information on which many others base their decisions. Efforts to address this corruption should focus on correcting large imbalances in the current political economy of medical knowledge. A sequestration of pharmaceutical research and development on one hand from pharmaceutical marketing on the other, though difficult to achieve, would address this and many other problems.
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 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 | 0.038 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| 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".