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Record W2150488698 · doi:10.1177/0009922814542608

Canadian Acute Respiratory Illness and Flu Scale (CARIFS) for Clinical Detection of Influenza in Children

2014· article· en· W2150488698 on OpenAlexaboutno aff
Jason Fischer, Priya A. Prasad, Susan Coffin, Elizabeth R. Alpern, Rakesh D. Mistry

Bibliographic record

VenueClinical Pediatrics · 2014
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDemographicsVenipunctureRespiratory illnessRespiratory systemProspective cohort studyInfluenza-like illnessEmergency departmentInternal medicineInfluenza A virusPediatricsCohortCohort studyRespiratory infectionVirusImmunologySurgeryDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Validated clinical scales, such as the Canadian Acute Respiratory Illness and Flu Scale (CARIFS), have not been used to differentiate influenza (FLU) from other respiratory viruses. METHODS: Secondary analysis of a prospective cohort presenting to the emergency department (ED) with an influenza-like infection from 2008 to 2010. Subjects were children aged 0 to 19 years who had a venipuncture and respiratory virus polymerase chain reaction. Demographics and CARIFS items were assessed during the ED visit; comparisons were made between FLU and non-FLU subjects. RESULTS: The 203 subjects had median age 30.5 months; 61.6% were male. Comorbid conditions (51.2%) were common. FLU was identified in 26.6%, and were older than non-FLU patients (69.7 vs 47.9 months, P = .02). Demographic, household factors, and mean CARIFS score did not differ between FLU (33.7), and non-FLU (32.0) (mean difference 1.6, 95% CI: -2.0 to 5.2) groups. CONCLUSIONS: CARIFS cannot discriminate between FLU and non-FLU infection in ED children with influenza-like infection.

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)0.002
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.105
GPT teacher head0.455
Teacher spread0.350 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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