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Record W2010551042 · doi:10.1177/1553350608318144

Risk Factors for Bile Duct Injury During Laparoscopic Cholecystectomy: A Case-Control Study

2008· article· en· W2010551042 on OpenAlexaffabout
Ramin Kholdebarin, Jonathan Boetto, Julie L. Harnish, David R. Urbach

Bibliographic record

VenueSurgical Innovation · 2008
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsUniversity of TorontoToronto General Hospital
Fundersnot available
KeywordsMedicineOdds ratioBile ductCholecystectomyConfidence intervalCystic ductComplicationLaparoscopic cholecystectomyLogistic regressionCase-control studySurgeryGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

Common bile duct injury is a serious but uncommon complication of laparoscopic cholecystectomy. A case-control epidemiologic study of patients who had undergone cholecystectomy in Ontario, Canada, between 1991 and 1997 was performed. Four patients who had undergone a laparoscopic cholecystectomy at the same hospital 2 months prior to a case were selected as controls. The risk of bile duct injury associated with various exposures was estimated by unconditional logistic regression. There were 28 cases and 88 controls. Emergency operation (adjusted odds ratio = 5.0; 95% confidence interval, 1.4-17.8) and failure to identify the cystic duct (adjusted odds ratio = 13.7; 95% confidence interval, 2.5-76.3) were statistically significant risk factors for operative bile duct injury. No other characteristics were independent risk factors for bile duct injury. Failure to identify the cystic duct and the emergency surgery are independent risk factors for bile duct injury.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.402

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.301
Teacher spread0.275 · 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 designObservational
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

Citations37
Published2008
Admission routes2
Has abstractyes

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