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Record W3026964364 · doi:10.1503/cmaj.75585

Participation of more community hospitals in randomized trials of treatments for COVID-19 is needed

2020· letter· en· W3026964364 on OpenAlexaffvenue
Jennifer Tsang, Alexandra Binnie, George Farjou, Dimitra Fleming, Maham Khalid, Erick Duan

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSt. Joseph’s Healthcare HamiltonWilliam Osler Health SystemMcMaster UniversityNiagara Health System
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Randomized controlled trial2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)LimitingMedicineClinical trialCoronavirus InfectionsAlternative medicineMEDLINESurgeryPathology

Abstract

fetched live from OpenAlex

In their CMAJ commentary, Cheng and colleagues highlight the importance of evaluating potential COVID-19 therapies systematically within randomized controlled trials (RCTs).[1][1] In addition to cost, another limiting factor for clinical trials is speed of enrolment. For researchers to derive robust

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)0.198
metaresearch head score (Gemma)0.474
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.985
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1980.474
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0020.002
Science and technology studies0.0070.012
Scholarly communication0.0130.023
Open science0.0110.008
Research integrity0.0840.084
Insufficient payload (model declined to judge)0.0220.010

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 head0.145
GPT teacher head0.481
Teacher spread0.336 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
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

Citations10
Published2020
Admission routes2
Has abstractyes

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