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Record W3125418851 · doi:10.1093/bjs/znaa043

Impact of COVID 19 on the provision of surgical services in Ontario, Canada: population-based analysis

2020· article· en· W3125418851 on OpenAlexaffabout
David Gómez, Fahima Dossa, Colin Sue‐Chue‐Lam, Andrew S. Wilton, Charles de Mestral, David R. Urbach, Nancy N. Baxter

Bibliographic record

VenueBritish journal of surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsInstitute for Clinical Evaluative SciencesPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)PandemicSurgical proceduresMedical emergencyAmbulatoryPopulationGovernment (linguistics)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakEmergency medicineSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

In this population-based analysis, the rate of surgical procedures decreased during the COVID period with hospitals rapidly reducing ambulatory and in-patient elective procedures in response to government directives. However, the observed and unexpected large reduction in urgent surgical procedures highlights the need to prioritize access to care for patients with emergency surgical conditions as part of pandemic planning.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.066
GPT teacher head0.337
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

Citations20
Published2020
Admission routes2
Has abstractyes

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