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Record W3111508700 · doi:10.1016/j.cjca.2020.11.016

Cardiovascular Care Delivery During the Second Wave of COVID-19 in Canada

2020· review· en· W3111508700 on OpenAlexaffvenueabout
Idan Roifman, Rakesh C. Arora, David Bewick, Chi-Ming Chow, Brian Clarke, Simone Cowan, Anique Ducharme, Kenneth Gin, Michelle M. Graham, Anil Gupta, Sean Hardiman, Michael Hartleib, Simon Jackson, Davinder S. Jassal, Mustapha Kazmi, Yoan Lamarche, Jean‐François Légaré, Howard Leong‐Poi, Samer Mansour, Ariane Marelli, Marc Ruel, Gary R. Small, Larry Sterns, Ricky D. Turgeon, Sean Virani, Harindra C. Wijeysundera, Kenny K. Wong, David Wood, Shelley Zieroth, Gurmeet Singh, Andrew D. Krahn

Bibliographic record

VenueCanadian Journal of Cardiology · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsWinnipeg Regional Health AuthorityDalhousie UniversityUniversity of OttawaMcGill University Health CentreCentre Hospitalier de l’Université de MontréalQueen Elizabeth II Health Sciences CentreProvincial Health Services AuthorityUniversity of AlbertaUniversité de MontréalMontreal Heart InstituteUniversity of British ColumbiaHealth Sciences CentreLibin Cardiovascular Institute of AlbertaRoyal Jubilee HospitalUniversity of CalgarySt. Boniface HospitalSunnybrook Health Science CentreSt. Michael's HospitalSaint John Regional HospitalUniversity of TorontoTrillium Health CentreUniversity of Manitoba
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus InfectionsPandemicBetacoronavirusIntensive care medicineVirologyInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.008
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: Review · Consensus signal: Review
Teacher disagreement score0.059
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.008
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.072
GPT teacher head0.323
Teacher spread0.251 · 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
GenreReview

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

Citations14
Published2020
Admission routes3
Has abstractno

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