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Record W4210606965 · doi:10.1503/cmaj.220081

Nirmatrelvir-ritonavir for COVID-19

2022· letter· en· W4210606965 on OpenAlexaffvenue
Emily G. McDonald, Todd C. Lee

Bibliographic record

VenueCanadian Medical Association Journal · 2022
Typeletter
Languageen
FieldMedicine
TopicInfectious Encephalopathies and Encephalitis
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)RitonavirComorbidity2019-20 coronavirus outbreakMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusMEDLINEDiseaseInternal medicineEmergency medicineIntensive care medicinePediatricsFamily medicineVirologyHuman immunodeficiency virus (HIV)Viral loadInfectious disease (medical specialty)OutbreakAntiretroviral therapyBiology

Abstract

fetched live from OpenAlex

Treatment is indicated for adult (≥ 18 yr) outpatients with nonhypoxic COVID-19 who are at high risk of severe disease progression (e.g., advanced age, comorbidity, unvaccinated or immunosuppressed).[1][1],[2][2] According to available data, patients with 5% risk of hospital admission have an

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)0.000
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0100.006

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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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
GenreEditorial

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

Citations31
Published2022
Admission routes2
Has abstractyes

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