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Record W2923602698 · doi:10.4103/cjrm.cjrm_28_18

Laparoscopic cholecystectomy for ultrasound normal gallbladders: Should we forego hepatobiliary iminodiacetic acid scans?

2019· article· en· W2923602698 on OpenAlexaffvenue
Judith Roger, Thomas Heeley, Wendy Graham, Anna Walsh

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineLaparoscopic cholecystectomyAbdominal ultrasoundUltrasoundCholecystectomyInformed consentBiliary dyskinesiaIminodiacetic acidRadiologyGeneral surgerySurgeryPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Hepatobiliary iminodiacetic acid (HIDA)-radionuclear scans are used to diagnose biliary dyskinesia, the treatment for which is a laparoscopic cholecystectomy (LC). However, the predictive value of the HIDA scan for LC candidacy is debated. CASE: A physical, ultrasound, and blood test for a 53-year-old woman with biliary dyskinesia-like symptoms were normal, contradicting a textbook history. A HIDA-scan was ordered but the results suggested she was not eligible for a LC. The patient insisted on receiving the procedure and gave informed consent to undergo an elective LC. RESULTS: Six-weeks post-surgery, the patient's symptoms had ceased besides one short episode of abdominal pain. CONCLUSION: A LC relieved the patient's symptoms, suggesting that negative HIDA-scans can mislead correct decisions to perform a LC. Surgeons who receive inconclusive HIDA scan results should consult their patients, and when necessary and agreed-upon, take an informed risk together in an attempt to improve the patient's quality of life.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.269
Teacher spread0.247 · 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 teacher head, not a consensus.

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

Citations2
Published2019
Admission routes2
Has abstractyes

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