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Record W2752959015 · doi:10.1093/ofid/ofx163.1859

An Assessment of the Validity of the Comprehensive Severity Index (CSI) as a Measure of Severity of Influenza Infection in Children

2017· article· en· W2752959015 on OpenAlexaff
Dat Tran, Susan E. Richardson, Moshe Ipp, Suzanne Schuh, Ari Bitnun, Andrew D. Paterson

Bibliographic record

VenueOpen Forum Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institutes of HealthBioFire Diagnostics
KeywordsMedicineLogistic regressionUnivariate analysisUnivariateSeverity of illnessInternal medicineReceiver operating characteristicPediatricsMultivariate analysisEmergency medicineMultivariate statistics

Abstract

fetched live from OpenAlex

A standardized quantitative severity score that reflects the breadth of influenza-related complications would prove valuable in epidemiologic analyses. The maximum CSI score (maxCSI) is a composite, continuous measure of illness severity, based on the degree of abnormality of individual signs and symptoms of a patient’s disease or diseases. Importantly, the index contains criteria for influenza as well as related complications. We evaluated the spectrum of influenza illness as measured by maxCSI and assessed its discriminatory power on 321 influenza-infected, otherwise healthy children (0–17 years) enrolled into a prospective study from the emergency department and inpatient units of a pediatric tertiary care hospital and an urban community pediatric clinic. The area under ROC curve (AUC) was computed for univariate (maxCSI as a sole predictor variable) and multivariable logistic regression models of two outcome measures: (1) influenza-related respiratory and extra-respiratory complications based on physician diagnosis and (2) hospitalization. Multivariable models incorporated maxCSI, age, household crowding, influenza type/subtype and antiviral therapy. For each outcome, the Hanley-McNeil method was used to compare AUCs of univariate and multivariable models. Of the 321 children enrolled, 200 (62.3%) were male and the median age was 5.25 years (range 0.07–17.96). 73 (22.7%) had complications while 61 (19.0%) were hospitalized; the median maxCSI was 25 (range 0–140). In univariate and multivariable modeling, maxCSI was significantly associated with both influenza-related complications and hospitalization (all P < 0.0001). The univariate models discriminated well between children with and without complications [AUC 0.88 (95% CI 0.83–0.93)] and between those who were and were not hospitalized [AUC 0.94 (95% CI 0.91–0.97)]. The AUCs for the corresponding multivariable models were not statistically significantly different: 0.90 (95% CI 0.86–0.94; P = 0.13) for complications and 0.94 (95% CI 0.91–0.97; P = 0.34) for hospitalization. The maxCSI represents a valid continuous outcome measure that can be leveraged to increase statistical power in epidemiologic studies aimed at identifying factors associated with severe influenza. All authors: No reported disclosures.

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)0.010
metaresearch head score (Gemma)0.023
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 score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.427
Teacher spread0.369 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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