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Record W4285540063 · doi:10.46747/cfp.6701e6

Challenges in the virtual assessment of COVID-19 infections in the community

2021· review· en· W4285540063 on OpenAlexaffvenue
Warren J. McIsaac, Ross Upshur, Sahana Kukan

Bibliographic record

VenueCanadian Family Physician · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 diagnosis using AI
Canadian institutionsLunenfeld-Tanenbaum Research InstituteSinai Health System
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PneumoniaMedicineCoronavirusIntensive care medicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Complication2019-20 coronavirus outbreakRespiratory failureIntensive careDiseaseVirologyInfectious disease (medical specialty)Internal medicineOutbreak

Abstract

fetched live from OpenAlex

Pneumonia is a serious complication of coronavirus disease 2019 (COVID-19) infection that can lead to respiratory failure, admission to intensive care, or death. While 81% of COVID-19 cases are considered mild,[1][1] this figure includes cases of pneumonia that were considered nonsevere based on

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)0.007
metaresearch head score (Gemma)0.024
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.230
GPT teacher head0.428
Teacher spread0.199 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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