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Record W4385399772 · doi:10.1503/cmaj.230246

Time for Canada to align with global innovations in treatment for tuberculosis

2023· article· en· W4385399772 on OpenAlexaffvenueabout
Adam R. Houston, Elizabeth Rea

Bibliographic record

VenueCanadian Medical Association Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsRoyal Roads UniversityPublic Health Ontario
Fundersnot available
KeywordsSAFERTuberculosisPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakMedicineKey (lock)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Directly Observed TherapyGlobal healthIntensive care medicineComputer scienceVirologyComputer securityPublic healthPathologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

KEY POINTS Despite serious disruptions to tuberculosis (TB) programs during the COVID-19 pandemic, including in Canada,[1][1] the past few years have also seen global advances in TB treatment. Novel drugs and regimens have been developed to support faster, safer, more effective treatment for drug-

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)0.006
metaresearch head score (Gemma)0.021
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.081
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0110.005
Scholarly communication0.0120.005
Open science0.0030.007
Research integrity0.0130.017
Insufficient payload (model declined to judge)0.0680.009

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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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