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Record W4318240666 · doi:10.3233/jrs-220043

The coverage of medical injuries in company trial informed consent forms

2023· article· en· W4318240666 on OpenAlexaff
David Healy, Augusto Germán Roux, Brianne Dressen

Bibliographic record

VenueInternational Journal of Risk & Safety in Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsClinical trialInformed consentNexus (standard)Alternative medicineMedicineFamily medicineEngineeringPathology

Abstract

fetched live from OpenAlex

Best practice consent forms in company clinical trials detail the financial coverage for medical treatment of injuries. In trials undertaken for licensing purposes these arrangements can raise concerns. We detail three cases in which elements of the consent forms appear misleading and designed to elicit a consent to participation that might not be forthcoming if volunteers for these clinical trials were aware that what is outlined in principle is not likely to happen in practice. Beyond clinical trial participants, these consent forms have wider implications. Medical coverage of injuries sustained in a clinical trial is a nexus where business, scientific and ethical considerations meet. It is not clear that anyone to date has grappled with the issues posed. This paper uses three clinical trials to illustrate the problems to be addressed.

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)0.005
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.398
Teacher spread0.363 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2023
Admission routes1
Has abstractyes

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