Undue inducement: a case study in CAPRISA 008
Bibliographic record
Abstract
: Participant safety and data integrity, critical in trials of new investigational drugs, are achieved through honest participant report and precision in the conduct of procedures. HIV prevention post-trial access studies in middle-income countries potentially offer participants many benefits including access to proven efficacious but unlicensed technologies, ancillary care that often exceeds local standards-of-care, financial reimbursement for participation and possibly unintended benefits if participants choose to share or sell investigational drugs. This case study examines the possibility that this combination of benefits may constitute an undue inducement for some participants in middle-income countries, where economic challenges are prevalent. A case study is presented of a single participant in a cohort of 382 participants who used concealment, fabrication and deception to ensure eligibility for a post-trial access study of an unlicensed HIV prevention technology at potential risk to her health and that of her fetus. A root cause analysis revealed her desire to access HIV prevention during an unplanned pregnancy with a partner whose faithfulness was in question. Researchers should consider implementation of systems to efficiently identify similar cases without inconveniencing the majority of participants TRIAL REGISTRATION NUMBER: NCT01691768.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".