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
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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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".