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Record W2600564521 · doi:10.1136/medethics-2016-103414

Undue inducement: a case study in CAPRISA 008

2017· article· en· W2600564521 on OpenAlexaff
Kathryn Mngadi, Jerome Amir Singh, Leila E. Mansoor, Douglas Wassenaar

Bibliographic record

VenueJournal of Medical Ethics · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersTides FoundationUnited States Agency for International Development
KeywordsDeceptionReimbursementClinical trialUndue influenceInformed consentHealth careMedicineUnintended consequencesInternet privacyBusinessFamily medicinePsychologyAlternative medicineComputer scienceSocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

: 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.

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)0.088
metaresearch head score (Gemma)0.451
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0880.451
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.037
Insufficient payload (model declined to judge)0.0000.000

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 head0.808
GPT teacher head0.713
Teacher spread0.095 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2017
Admission routes1
Has abstractyes

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