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Record W1966526194

Is HIDA Scan Necessary for Sonographically Suspicious Cholecystitis

2013· article· en· W1966526194 on OpenAlexvenueno aff
Irina Bernescu, Oliver S. Eng, Lindsay Potdevin, Rosebel Monteiro, Jeffrey Mino, Eric I. Chang, Tomer Davidov

Bibliographic record

VenueJournal of Current Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAcute cholecystitisCholecystitisUltrasoundAbdominal ultrasoundRadiologyCholecystectomyPredictive valueComputed tomographyGallbladderSurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Background : Historically, abdominal ultrasound is the study of choice for diagnosing cholelithiasis, whereas HIDA scan is the study of choice for diagnosing acute cholecystitis. With improvements in ultrasound technology over the past two decades, we sought to reevaluate the clinical utility of HIDA scan over ultrasound alone in diagnosing cholecystitis. Methods : A retrospective review of 154 patients admitted to our emergency room with suspicion for cholecystitis who underwent abdominal sonography, HIDA scan, and proceeded to cholecystectomy on the same admission was conducted. Results of ultrasound and HIDA scan were compared to the final surgical pathology. Results : The two groups did not differ with respect to age or gender. HIDA scan had a greater sensitivity, specificity, and accuracy in diagnosing cholecystitis, but the positive predictive value of ultrasound and HIDA scan were similar. Conclusions : While HIDA scan may be the test of choice for diagnosing cholecystitis, it may be unnecessary in patients with sonographically suspicious cholecystitis, as these sonographic features, when present, are highly predictive of cholecystitis. doi: http://dx.doi.org/10.4021/jcs201w

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.309
Teacher spread0.271 · 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.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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