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Emergency department presentation changes due to the coronavirus disease pandemic in Nova Scotia, Canada

2022· article· en· W4308329083 on OpenAlexaffabout
Tara Dahn, Patrick T. Fok, Hana Wiemer, Daniel J. Dutton, Valancy Cole, David A. Lewis, Tong Liu, Keith R. Brunt, Robert E. Hanlon, Jacqueline Fraser, C. Vaillancourt, Paul Atkinson

Bibliographic record

VenueWorld Journal of Emergency Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsHorizon Health NetworkSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsNova scotiaMedicinePandemicEmergency departmentCoronavirus disease 2019 (COVID-19)Presentation (obstetrics)CoronavirusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMedical emergencyFamily medicinePediatricsDiseaseVirologyInfectious disease (medical specialty)PathologyHistoryPsychiatryEthnologySurgeryOutbreak

Abstract

fetched live from OpenAlex

Multiple studies have reported decreased emergency department (ED) patient volumes during the coronavirus disease (COVID-19) pandemic, Most existing studies have investigated general trends in ED presentations and have not examined the impact of COVID-19 on different types of EDs, specific ED patient groups, or illness presentations. This study is a retrospective observational study of changes in ED volumes during the first wave of the COVID-19 pandemic in Nova Scotia (NS). NS is an Atlantic Province in Canada with a population of 979,351. [8] The Government of NS declared a state of emergency on March 22, 2020, to help contain the spread of COVID-19. By June 22, 2020, the end of the first wave of COVID-19 in the province, there had been a total of 1,061 cases of COVID-19 and 65 deaths from the disease. [9]

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.000

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 head0.143
GPT teacher head0.432
Teacher spread0.289 · 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
Published2022
Admission routes2
Has abstractyes

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