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Informed Consent: Physician Inexperience is a Material Risk for Patients

2007· article· en· W2085846327 on OpenAlexaffabout
Richard Veerapen

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2007
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInterpretation (philosophy)Informed consentFull disclosureMedicineFamily medicineMedical emergencyLawPolitical scienceAlternative medicinePathologyComputer security

Abstract

fetched live from OpenAlex

This paper examines the case for an expanded interpretation of the concept of "material risk" such that it necessitates voluntary disclosure of physician inexperience with a specific medical procedure. Informed consent law in the United States, Canada, and most commonwealth jurisdictions has become a driver of standards of risk disclosure by physicians during the informed consent process. The legal standard of risk disclosure expected of a physician hinges on the interpretation of the entity called "material risk." Any impairment of the physician related to drug usage, disease, or alcohol which compounds the risk of a procedure is very likely to be considered material by a patient. This paper argues that physician inexperience is a factor that a reasonable patient would attach significance to and that it should therefore be viewed as a "material risk" requiring disclosure.

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)0.047
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.125
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.029
Scholarly communication0.0050.008
Open science0.0020.008
Research integrity0.0140.011
Insufficient payload (model declined to judge)0.0050.001

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 head0.196
GPT teacher head0.534
Teacher spread0.338 · 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 designTheoretical or conceptual
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

Citations9
Published2007
Admission routes2
Has abstractyes

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