The Human Subjects Trade: Ethical and Legal Issues Surrounding Recruitment Incentives
Bibliographic record
Abstract
Over the past 5 years, a series of articles in leading American newspapers has revealed the extent to which the conduct of clinical trials may be affected by inducements offered by corporate research sponsors and accepted by some unscrupulous physicians. The cases described were disturbing. They involved physicians engaged in excessive “enrollment activities” in exchange for money. Some of these physicians perpetrated fraud, falsifying their recruitment records in order to increase their profits. Others ignored exclusion criteria designed to ensure the safety of subjects and the validity of research results, referring their patients to research investigating treatments for conditions from which they did not suffer. One of the articles reports that physicians focusing exclusively on commercial research regularly divulge annual incomes upwards of $1,000,000 with profits in excess of $300,000. Two physicians accumulated well over $10,000,000 through clinical trials activities in less than a decade.
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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 | 0.375 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.057 | 0.030 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".