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Record W4398194181 · doi:10.1177/00031348241256064

Cost, Operative Delay, and X-Rays for Incorrect Surgical Counts

2024· article· en· W4398194181 on OpenAlex
Megan Melland‐Smith, Jenny H. Chang, Varisha Essani, Sara M. Maskal, Ryan C. Ellis, Lucas Beffa, Clayton C. Petro, Ajita S. Prabhu, David M. Krpata, Benjamin T. Miller, Michael Rosen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe American Surgeon · 2024
Typearticle
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsNorth York General HospitalUniversity of Toronto
Fundersnot available
KeywordsRust (programming language)Code (set theory)MedicineProtocol (science)WorkflowSurgeryGeneral surgeryComputer scienceRadiologyPathologyProgramming language

Abstract

fetched live from OpenAlex

At Cleveland clinic, an incorrect surgical count triggers Code Rust; a protocol that mandates an intraoperative patient X-ray, staff radiology read, and discussion with the surgeon before the incision is closed. Code Rust calls from November 2014 to December 2022 were retrospectively reviewed. Realtime workflow and operative details of Code Rust cases were analyzed.1277 Code Rusts were identified. Average time from ordering the X-ray to final radiology report was 50 minutes, totalling $2,362,450.00 spent on operating room time. Code Rust was called twice as frequently during urgent or emergent cases, compared to elective. There were more staff in Code Rust rooms compared to non-Code Rust rooms. A foreign body on X-ray was identified in 42/1277 (3.3%) cases. Code Rust is a resource intensive process that is more common in emergent cases that involve multiple staff. While retained foreign bodies are identified in a small percentage of cases, the current system should be revisited to reduce operating time and expense.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.040
GPT teacher head0.365
Teacher spread0.325 · 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