Estimating Respiratory Syncytial Virus-associated Hospitalization in the First Year of Life Among Infants Born at 32–35 Weeks of Gestation
Bibliographic record
Abstract
BACKGROUND: Prophylaxis against respiratory syncytial virus-associated hospitalization (RSV-H) with anti-RSV monoclonal antibody is not considered cost-effective for routine use in most jurisdictions. The aim of this study was to develop a scoring tool to estimate local risk of RSV-H in the first year of life among moderately premature infants to assist in prophylaxis decision making. METHODS: A retrospective cohort was constructed from population-based databases in Nova Scotia, Canada, to follow 32- to 35-week gestation infants from the prenatal period to <12 months of age, from 1998 to 2008. Potential risk factors were entered into the logistic regression model, where the dependent variable was RSV-H. Receiver operator characteristic analysis demonstrated cutoff scores producing the highest predictive accuracy, and the likelihood ratio test was used to select the final set of variables for the predictive tool. RESULTS: In 2811 eligible infants, the overall RSV-H rate was 3.1% (88/2811). Of 17 variables considered, 3 were used to create the scoring tool: birth during December to February, household smoke exposure and household crowding. The positive likelihood ratios of predictive tool scores for high, moderate and low of RSV-H were 3.57, 3.38 and 1.95, whereas posttest probabilities (risk of RSV-H) were 11.4%, 10.8% and 1.6%, respectively. CONCLUSIONS: While able to predict infants at low risk of RSV-H, the tool did not discriminate high from moderate risk infants. The tool could be used in anticipatory care to help educate families about reducing risk of serious RSV illness in their newborn.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".