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Record W4393104457 · doi:10.1097/mcp.0000000000001066

Keeping global focus on the many challenges of respiratory tract infections

2024· article· en· W4393104457 on OpenAlexaff
Alimuddin Zumla, David S.C. Hui

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 diagnosis using AI
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineRespiratory tract infectionsIntensive care medicineFocus (optics)Respiratory tractMEDLINERespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

aCentre for Clinical Microbiology, Division of Infection and Immunity, University College London bNIHR Biomedical Research Centre, UCL Hospitals NHS Foundation Trust, London, UK cDepartment of Medicine & Therapeutics and SH Ho Research Centre for Infectious Diseases, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China Correspondence to David S. Hui, MD., FRCP, Department of Medicine & Therapeutics & SH Ho Research Center for Infectious Diseases, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, New Territories, Hong Kong. E-mail: [email protected]

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)0.008
metaresearch head score (Gemma)0.015
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 score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0080.011
Open science0.0010.007
Research integrity0.0070.013
Insufficient payload (model declined to judge)0.0200.010

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 head0.141
GPT teacher head0.414
Teacher spread0.273 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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