An Assessment of the Validity of the Comprehensive Severity Index (CSI) as a Measure of Severity of Influenza Infection in Children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".