MétaCan
Menu
Back to cohort
Record W3049124077 · doi:10.1016/j.aucc.2021.06.015

Development and validation of a tool to appraise guidelines on SARS-CoV-2 infection control strategies in healthcare workers

2021· article· en· W3049124077 on OpenAlexaff
Ashwin Subramaniam, Mallikarjuna Ponnapa Reddy, Umesh Kadam, Alexander Zubarev, Zheng Jie Lim, Chris Anstey, Shailesh Bihari, Jumana Haji, Jinghang Luo, Saikat Mitra, Kollengode Ramanathan, Arvind Rajamani, Francesca Rubulotta, Erik Svensk, Kiran Shekar

Bibliographic record

VenueAustralian Critical Care · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsASTER
FundersMetro North Hospital and Health Service
KeywordsMedicineInfection controlDelphi methodGuidelinePersonal protective equipmentHealth carePandemicSpecialtyDelphiMedical emergencyDiseaseCoronavirus disease 2019 (COVID-19)Intensive care medicineFamily medicinePathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.127
metaresearch head score (Gemma)0.342
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.670

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.342
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0160.006
Science and technology studies0.0020.001
Scholarly communication0.0100.005
Open science0.0030.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.100
GPT teacher head0.419
Teacher spread0.319 · 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.

Study designBench or experimental
DomainEvaluation
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

Citations4
Published2021
Admission routes1
Has abstractno

Explore more

Same venueAustralian Critical CareSame topicInfection Control and VentilationFrench-language works237,207