Value of a risk scoring tool to predict respiratory syncytial virus disease severity and need for hospitalization in term infants
Bibliographic record
Abstract
Several environmental and demographic risk factors have been validated and are used to determine the risk of acquiring severe respiratory syncytial virus (RSV) infection and subsequent hospitalization in late preterm infants born at 33-35 weeks gestational age. The applicability of the same composite model of risk factors in the term population has not been fully explored. The primary objective of this pilot study was to establish whether a risk scoring tool (RST), could predict the severity of RSV infection in term, RSV-positive infants who were hospitalized. A retrospective observational study was conducted in a pediatric unit, over 2 RSV seasons (2011-2013). A convenient sample of 72 children was selected out of a total of 111 RSV-positive cases after exclusions. The RST was applied and a score of respiratory disease severity was determined for each patient. Demographic characteristics were analyzed by standard descriptive methods, χ(2) analysis was utilized for categorical data and ANOVA for comparison between the clinical severity groups and the RST score. A P-value <0.05 was considered significant. Sixty per cent (n = 43) of all infants scored in the low-risk category compared to 26% (n = 19) in the moderate and 14% (n = 10) in the high-risk groups. RST scores were also inconsistent with disease severity. Mean (SD) RST scores for those with mild, moderate, and severe illness were 47.8 [16.4], 41.1 [20.39] and, 41.7 [19.8], respectively (P = 0.17). In conclusion, the RST did not predict accurately the clinical severity of RSV bronchiolitis in term infants nor did it correlate with risk for RSV-related hospitalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".