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Record W2469441829 · doi:10.1177/1556264616650117

Ethics Oversight Mechanisms for Surgical Innovation

2016· review· en· W2469441829 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Empirical Research on Human Research Ethics · 2016
Typereview
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersCanadian Institutes of Health Research
KeywordsSpeculationArgumentation theoryArgument (complex analysis)Engineering ethicsValue (mathematics)Research ethicsMechanism (biology)Systematic reviewEpistemologyMedicinePsychologyMEDLINEPolitical scienceBusinessLawComputer sciencePhilosophyEngineering

Abstract

fetched live from OpenAlex

Surgical innovation typically falls under the purview of neither conventional clinical ethics nor research ethics. Due to a lack of oversight for surgical innovation-combined with a potential for significant risk-a wide range of arguments has been advanced in the literature to support or undermine various oversight mechanisms. To scrutinize the argumentation surrounding oversight options, we conducted a systematic review of published arguments. We found that the arguments are typically grounded in common sense and speculation instead of evidence. Presently, the justification or superiority for any single oversight mechanism for surgical innovation cannot be established convincingly. We suggest ways to improve the argument-based literature and discuss the value of systematic reviews of arguments and reasons.

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.

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)0.150
metaresearch head score (Gemma)0.069
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1500.069
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0070.005
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0080.055
Insufficient payload (model declined to judge)0.0010.000

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 head0.887
GPT teacher head0.733
Teacher spread0.155 · 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