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Record W2026647402 · doi:10.1080/08989621.2013.866045

Disclosure, Consent, and the Exercise of Patient Autonomy in Surgical Innovation: A Systematic Content Analysis of the Conceptual Literature

2014· review· en· W2026647402 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

VenueAccountability in Research · 2014
Typereview
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill UniversityUniversité de MontréalMontreal Clinical Research Institute
FundersCanadian Institutes of Health ResearchSociety of University Surgeons
KeywordsInformed consentAutonomyPsychologyTerminologyAmbiguityConceptual frameworkHarmMedicineSocial psychologyAlternative medicineSociologyPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

The classification of surgical innovation as clinical care, research, or as third distinct type of activity creates ambiguity which impacts standards for disclosure and informed consent. We conducted a systematic review of the conceptual literature to identify positions expressed about consent and disclosure, as well as major tension points associated with this issue. Literature overwhelmingly favors special consent and disclosure. Four major tension points were identified: the use of biasing/biased terminology to characterize innovation; patient vulnerability; the relationship between surgeon-innovator and patient; and practices and associated gaps related to consent and disclosure. Recommendations often focused on the informed consent process.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.057
metaresearch head score (Gemma)0.074
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.199
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.074
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.011
Science and technology studies0.0000.007
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.006
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 head0.705
GPT teacher head0.620
Teacher spread0.085 · 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