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Record W4410566266 · doi:10.1177/08404704251338668

Cost-analysis and rationale for implementing semi-urgent laparoscopic cholecystectomy programs in a public healthcare system

2025· article· en· W4410566266 on OpenAlexaffabout
Victoria Ivankovic, Dexter Choi, Shahad Abdulkhaleq Mamalchi, Peter Glen, Maher Matar, Fady Balaa

Bibliographic record

VenueHealthcare Management Forum · 2025
Typearticle
Languageen
FieldMedicine
TopicGallbladder and Bile Duct Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineLaparoscopic cholecystectomyCholecystectomyHealth careGeneral surgeryMedical emergencyHealthcare systemEmergency surgeryPublic healthcarePublic hospitalIntervention (counseling)Emergency medicineIntensive care medicineSurgeryPublic healthNursing

Abstract

fetched live from OpenAlex

Wait times for elective surgical procedures in publicly funded healthcare systems impede patient well-being and resource efficiency. Patients with gallstone disease requiring semi-urgent intervention are often treated via inpatient emergency pathways due to limited elective surgery access. This study aimed to evaluate the rationale and cost-effectiveness of providing timely outpatient semi-urgent cholecystectomy. We retrospectively reviewed 512 patients with urgent biliary disease (excluding cholecystitis) who underwent surgery between July 2019 and December 2022. The primary outcome was time from booking to operating room; the secondary was the estimated cost of prolonged hospital stays. Patients waited an average of 26.45 hours; 19.1% waited 48 hours or longer, and 6.2% waited 72 hours or more. The associated cost was $405,785 over 40 months. Implementing semi-urgent surgical resources could reduce costs, improve efficiency, and enhance patient quality of life. Future work should involve stakeholders to address barriers and facilitators in Canada.

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)0.003
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.058
GPT teacher head0.340
Teacher spread0.281 · 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 designNot applicable
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

Citations0
Published2025
Admission routes2
Has abstractyes

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