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Record W2111141594 · doi:10.1017/cjn.2014.26

Retrait de consentement et chirurgie éveillée : illustration et enjeux éthiques

2014· article· fr· W2111141594 on OpenAlexvenueno aff
Michel Wager, Foucaud Du Boisguéheneuc, Coline Bouyer, Claudette Pluchon, Véronique Stal, Roger Gil

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2014
Typearticle
Languagefr
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecologyHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

RÉSUMÉ Contexte Le développement au cours des deniers années des interventions neurochirurgicales sous anesthésie locale, en particulier dans le champ neuro-oncologique, rend possible la question d’une éventuelle révocation per opératoire du consentement du patient à la procédure chirurgicale. Observation Les auteurs décrivent la révocation de son consentement par un patient au cours de l’exérèse chirurgicale d’un gliome de bas grade fronto-temporo-insulaire droit. Discussion Les aspects spécifiques aux conditions de révocation du consentement dans le contexte particulier de la chirurgie sous anesthésie locale sont discutés. La possibilité d’un pacte d’Ulysse est proposée et discutée. Conclusions Les interventions chirurgicales en condition éveillée créent un contexte particulier en ce qu’une révocation du consentement per opératoire devient possible. Dans ces conditions, l’information préopératoire pourrait aussi aborder avec le malade la question de la conduite à tenir au cas où il serait amené à solliciter un retrait de consentement lors de son réveil per opératoire.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.014
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.113
GPT teacher head0.391
Teacher spread0.279 · 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 designCase report
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

Citations1
Published2014
Admission routes1
Has abstractyes

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