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Record W1940010674 · doi:10.1111/dewb.12087

Attitudes toward Post‐Trial Access to Medical Interventions: A Review of Academic Literature, Legislation, and International Guidelines

2015· review· en· W1940010674 on OpenAlexfundno aff
Kori Cook, Jeremy Snyder, John Calvert

Bibliographic record

VenueDeveloping World Bioethics · 2015
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchSimon Fraser University
KeywordsLegislationPsychological interventionPolitical sciencePublic relationsClinical trialMedicinePublic administrationNursingLaw

Abstract

fetched live from OpenAlex

There is currently no international consensus around post-trial obligations toward research participants, community members, and host countries. This literature review investigates arguments and attitudes toward post-trial access. The literature review found that academic discussions focused on the rights of research participants, but offered few practical recommendations for addressing or improving current practices. Similarly, there are few regulations or legislation pertaining to post-trial access. If regulatory changes are necessary, we need to understand the current arguments, legislation, and attitudes towards post-trial access and participants and community members. Given that clinical trials conducted in low-income countries will likely continue, there is an urgent need for consideration of post-trial benefits for participants, communities, and citizens of host countries. While this issue may not be as pressing in countries where participants have access to healthcare and medicines through public schemes, it is particularly important in regions where this may not be available.

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 imitation

Not 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.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.116
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.051
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.116
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0080.010
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.882
GPT teacher head0.661
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2015
Admission routes1
Has abstractyes

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