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Record W1985697828 · doi:10.1002/cncr.23999

Timing of consent for the research use of surgically removed tissue

2008· article· en· W1985697828 on OpenAlexaff
Robert E. Hewitt, Peter H. Watson, Rajiv Dhir, Roger Aamodt, Gerry Thomas, Dan Mercola, William E. Grizzle, Manuel M. Morente

Bibliographic record

VenueCancer · 2008
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsBC Cancer Agency
FundersNational Cancer Institute
KeywordsInformed consentDocumentationDignityMedicineAutonomySurgeryLawAlternative medicinePolitical sciencePathologyComputer science

Abstract

fetched live from OpenAlex

Consent by patients to perform surgery (‘surgical consent’) and consent for the research use of residual tissue (‘research consent’) are desirable to respect individual autonomy and human dignity. In the past, documentation of these consents has been conveniently obtained before surgery by the same person using the same form. More recently, however, ethical concerns have forced a separation between the 2 consents so that they are now often obtained by different people using different forms, thus raising the possibility of obtaining the research consent postoperatively. The current study seeks to clarify the issues and explain why a postoperative informed consent process has distinct advantages in certain circumstances.

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)0.085
metaresearch head score (Gemma)0.154
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.448

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.154
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0030.004
Scholarly communication0.0040.005
Open science0.0020.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0200.009

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 head0.824
GPT teacher head0.678
Teacher spread0.146 · 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.

Study designNot applicable
DomainMethods
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

Citations24
Published2008
Admission routes1
Has abstractyes

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