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Record W3092488561 · doi:10.1503/cjs.013520

Redesigning operating room booking in a tertiary care academic centre during the COVID-19 pandemic

2020· article· en· W3092488561 on OpenAlexafffundvenue
Michael Tänzer, Stella Racaniello, Liane S. Feldman

Bibliographic record

VenueCanadian Journal of Surgery · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsMcGill University Health Centre
FundersMcGill University
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)TriageMedical emergencySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Prioritization2019-20 coronavirus outbreakMultidisciplinary approachFlexibility (engineering)Emergency medicineOperations managementInfectious disease (medical specialty)DiseaseProcess managementOutbreakVirology

Abstract

fetched live from OpenAlex

SUMMARY: With the closure of most operating rooms (ORs) during the coronavirus disease 2019 (COVID-19) pandemic, the traditional allocation of block OR time needed to be redesigned. An important factor permitting the treatment of patients in a prioritized fashion was our pre-existing centralized OR booking (CORB) framework, which already required surgeons to categorize the priority level for each patient. The CORB, in conjunction with the multidisciplinary OR oversight committee that was formed during COVID-19 to review and triage the urgent cases, allowed for prioritization of cases among surgical services. Centralized OR booking provided opportunities that were essential in OR planning during the pandemic, including the ability to plan surgeries to maximize OR efficiency, minimize the number of admissions on any given day to the wards and the intensive care unit, flatten the number of admissions over the week and provide the flexibility to ramp up or down the number of ORs as the crisis changed.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.162
GPT teacher head0.388
Teacher spread0.227 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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