PREDICTIVE VALUE OF THE RESPIRATORY SYNCYTIAL VIRUS RISK SCORING TOOL IN THE TERM INFANT
Bibliographic record
Abstract
Objective To determine if a validated, risk-scoring tool (RST), developed by the Canadian PICNIC group to target moderate and high-risk, 33–35-week gestational age infants for immunisation during the respiratory syncytial virus (RSV) season would also predict RSV hospitalisation and emergency room visits in term infants with RSV infection. Methods 72 RSV-positive cases were identified retrospectively, during the 2006–2007 RSV season. A questionnaire/chart review was conducted on 68/72 cases (four declined consent), to determine risk categories based on the RST. Four trained abstractors extracted pertinent data from the medical records of RSV-positive patients. Mean, SD, and percentages were used to describe study variables for hospitalised and emergency room patients. Chi-square was used for the analysis of categorical data and analysis of variance for comparisons within and between groups. Results The majority of infants scored in the low-risk category (n = 44), versus moderate (n = 16) or high risk (n = 8). The mean RST scores for those admitted to the paediatric intensive care unit/ward, the ward only, or those discharged home from the emergency room were 48.3, 40.9, and 35.5, respectively. The mean number of risk factors for those discharged home versus those admitted was 2.5 (SD 1.3) and 2.97 (SD 1.13), respectively (p = 0.15). Only two out of eight cases in the high-risk group required intensive care. Conclusions Overall, the RST did not discriminate the risk of emergency room visits or RSV hospitalisation in term infants. A larger study is necessary to establish risk factors that more accurately determine RSV hospitalisation among term infants in order to target palivizumab prophylaxis cost-effectively.
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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 | 0.014 |
| 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 | 0.001 |
| 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".