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Record W3132187920 · doi:10.46747/cfp.6702112

The challenges of measuring blood pressure during COVID-19

2021· article· en· W3132187920 on OpenAlexaffvenue
Martin Dawes, Stephen Beerman, Mark Gelfer, B. M. Hobson, Nadia Khan, Laura M. Kuyper, Birinder Mangat, Karen C. Tran, Marnie Goodwin Wilson, Janusz Kaczorowski

Bibliographic record

VenueCanadian Family Physician · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationCentre Hospitalier de l’Université de MontréalUniversity of British Columbia
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Face masksMedicineCoronavirus InfectionsCoronavirusDiseaseFace (sociological concept)Computer scienceChronic diseaseDisease managementIntensive care medicineData scienceVirologyPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Coronavirus disease 2019 (COVID-19) has altered the availability of face-to-face care in family practice. The negative effect on routine care for chronic disease management is an ongoing challenge. The purpose of this article is to describe some of those challenges and to suggest solutions. High

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 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)0.016
metaresearch head score (Gemma)0.063
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.691
Threshold uncertainty score0.621

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.063
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.081
GPT teacher head0.314
Teacher spread0.233 · 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
GenreCommentary

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

Citations13
Published2021
Admission routes2
Has abstractyes

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