Comparison of the Canadian vs. the international risk scoring tool for respiratory syncytial virus prophylaxis in moderate-to-late preterm infants
Bibliographic record
Abstract
Aim The study objective was to compare the Pediatric Investigators Collaborative Network on Infections in Canada risk scoring tool (CRST) that determines need for respiratory syncytial virus (RSV) prophylaxis in infants 33–35 weeks gestational age during the RSV season, with the newly developed international risk scoring tool (IRST). Methods Children 33–35 weeks gestational age born during the 2018–2021 RSV seasons were prospectively identified following birth and scored with the validated CRST and IRST, that comprises seven and three variables respectively, into low- moderate- and high-risk groups that predict RSV-related hospitalization. Correlations between total scores on the two tools, and cut-off scores for the low-, moderate- and high-risk categories were conducted using the Spearman rank correlation. Results Over a period of 3 RSV seasons, 556 infants were scored. Total risk scores on the CRST and the IRST were moderately correlated (rs = 0.64, p < 0.001). A significant relationship between the risk category rank on the CRST and the risk category rank on the IRST (rs = 0.53; p < 0.001) was found. The proportion of infants categorized as moderate risk for RSV hospitalization by the CRST and IRST were 19.6% (n = 109) and 28.1% (n = 156), respectively. Conclusion The IRST may provide a time-efficient scoring alternative to the CRST with three vs. seven variables, and it selects a larger number of infants who are at moderate risk for RSV hospitalization for prophylaxis. A cost-utility analysis is necessary to justify country-specific use of the IRST, while in Canada a cost comparison is necessary between the IRST vs. the currently approved CRST prior to adoption.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".