Can we improve the targeting of respiratory syncytial virus (RSV) prophylaxis in infants born 32–35 weeks’ gestational age with more informed use of risk factors?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the key risk factors for respiratory syncytial virus (RSV) hospitalisation in 32-35 weeks' gestational age (wGA) infants. METHODS: Published risk factors were assessed for predictive accuracy (area under the receiver operating characteristic curve [ROC AUC]) and for number needed to treat (NNT). RESULTS: Key risk factors included: proximity of birth to the RSV season; having siblings; crowding at home; day care; smoking; breast feeding; small for GA; male gender; and familial wheezing/eczema. Proximity of birth to the RSV season appeared the most predictive. Risk factors models from Europe and Canada were found to have a high level of predictive accuracy (ROC AUC both > 0.75; NNT for European model 9.5). A model optimised for three risk factors (birth ± 10 weeks from start of RSV season, number of siblings ≥ 2 years and breast feeding for ≤ 2 months) had a similar level of prediction (ROC AUC: 0.776; NNT: 10.2). An example two-risk factor model (day care attendance and living with ≥ 2 siblings < 5 years old) had a lower level of predictive accuracy (ROC AUC: 0.55; NNT: 26). CONCLUSIONS: An optimised combination of risk factors has the potential to improve the identification of 32-35 wGA infants at heightened risk of RSV hospitalisation.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".