MétaCan
Menu
Back to cohort
Record W2953439757 · doi:10.1016/j.eclinm.2019.06.013

Rethink Respiratory Rate for Diagnosing Childhood Pneumonia

2019· article· en· W2953439757 on OpenAlexaff
Israel Amirav, Moran Lavie

Bibliographic record

VenueEClinicalMedicine · 2019
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicinePneumoniaPediatricsChild mortalityScopusRespiratory infectionFamily medicineIntensive care medicineMEDLINEPopulationEnvironmental healthRespiratory systemInternal medicine

Abstract

fetched live from OpenAlex

One in six childhood deaths is caused by pneumonia, making it the largest infectious cause of death for children worldwide [1], particularly in low and middle-income countries (LMIC) where timely pneumonia diagnosis is a much greater challenge because of limited resources [2]. The World Health Organization (WHO) defines pneumonia as the presence of fast breathing and/or chest indrawing in children who present with cough or cold and/or difficulty breathing. If diagnosed early, antibiotic therapy can be initiated to effectively treat pneumonia [3].

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)0.011
metaresearch head score (Gemma)0.073
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 score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0030.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.009

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 head0.033
GPT teacher head0.349
Teacher spread0.316 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEClinicalMedicineSame topicPneumonia and Respiratory InfectionsFrench-language works237,207