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Record W6942402006 · doi:10.14288/1.0432789

Paxlovid in British Columbia Interim real-world analysis

2023· article· en· W6942402006 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsInterimChristian ministryDocumentationInterim analysisEmergency departmentPublic healthMEDLINE

Abstract

fetched live from OpenAlex

Background: Therapeutics Letter 141 summarizes an interim analysis of the use of the antiviral medication Paxlovid (nirmatrelvir/ritonavir) in the Canadian province of British Columbia (BC). Paxlovid was authorized for emergency use by Health Canada in December 2021, in response to the COVID-19 pandemic. Methods: The Therapeutics Initiative’s (TI) Pharmacoepidemiology group examined the 28-day risk of Covid-19-related hospitalization or death from any cause using BC Ministry of Health datasets including PharmaNet, emergency department encounters, physician billing codes, discharge abstracts, PCR testing, and vaccination status. Patient follow-up data collected by community pharmacists was also assessed. Results: The review found that the evidence on Paxlovid is limited and of low certainty, due to issues with study design, conduct, and reporting. The available evidence suggests Paxlovid may reduce risk of hospitalization and death in some high-risk patients with COVID-19, but that the magnitude of this effect is uncertain and may be irrelevant to BC patients in 2023. Conclusions: The use of Paxlovid in BC should be guided by a careful consideration of the available evidence. Benefits have not been proven for lower-risk patients. PharmaCare’s program to record adverse drug events may lead to better methods of documentation in the future.

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)0.035
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.373
Threshold uncertainty score0.750

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.111
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0050.011
Science and technology studies0.0010.001
Scholarly communication0.0050.001
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.025
GPT teacher head0.271
Teacher spread0.245 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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