Development and Validation of a Risk Scoring Tool to Predict Respiratory Syncytial Virus Hospitalization in Premature Infants Born at 33 through 35 Completed Weeks of Gestation
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Bibliographic record
Abstract
OBJECTIVE: The purpose of the study was to develop and validate a clinical instrument predicting the risk of respiratory syncytial virus (RSV)-associated hospitalization (RSV-H) in premature infants born at 33 through 35 completed weeks of gestation (33-35GA). DESIGN: An RSV risk scoring tool (RSV-RS) was developed by entering risk factors for RSV-H, determined in a Canadian prospective study, into a multiple logistic regression model. The scoring tool was then validated externally with data from a Spanish case-control study (FLIP). The Canadian cohort comprised 1758 RSV-positive infants born 33-35GA, of whom 66 (3.7%) had confirmed RSV-H. The FLIP data set comprised 186 (33.4%) RSV-H cases and 371 (66.7%) controls. METHOD: The primary outcome measure was RSV-H. The RSV-RS score was the sum of the weighted probabilities for each included risk factor multiplied by 100 and ranged from 0 to 100. Receiver operator characteristic curve analyses determined cutoff points to predict subjects at low, moderate, or high RSV-H risk. RESULTS: The RSV-RS included 7 risk factors and cutoff scores of 0-48, 49-64, and 65- 100 for low-, moderate-, and high-risk subjects, respectively. For the Canadian cohort, RSV-RS sensitivity in predicting RSV-H cases was 68.2%, with 71.9% specificity. With the FLIP data set, the RSV-RS had lower accuracy (61.3% sensitivity; 65.8% specificity) but showed significant positive association with increased risk for RSV-H. CONCLUSION: The RSV-RS accurately identified 33-35GA infants at increased risk for RSV-H in a Canadian cohort. External validation with Spanish case-control study data further confirmed that the scoring tool is appropriate for the estimation of RSV-H risk.
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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 | 0.001 | 0.007 |
| 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 | 0.000 | 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 it