Validation of the ROX index to predict high flow nasal cannula therapy treatment failure in infants with bronchiolitis
Bibliographic record
Abstract
Abstract Purpose High-flow nasal cannula (HFNC) therapy is commonly used to provide an intermediate level of respiratory support for infants with bronchiolitis. The ratio of SpO2/FiO2 to respiratory rate (ROX index) has previously been shown to aid in prediction of HFNC treatment failure in adults and children with diverse respiratory diseases. We aimed to evaluate the utility of the ROX index in predicting HFNC treatment failure in infants with bronchiolitis. Methods Retrospective analysis of previously well infants (< 1 year) hospitalized for bronchiolitis and initiated on HFNC as their primary modality of respiratory support. Results Of 64 infants (median age 70 days), 5 (7.8%) required intubation within 6 hours of HFNC initiation (median time to intubation 225 minutes; interquartile range 125–290 minutes). No between-group differences were observed with respect to sex, age, weight, respiratory syncytial virus infection status, presumed bacterial pneumonia, hospital unit of HFNC initiation, or respiratory parameters at initiation and 1 hour following. Compared to infants who were successfully treated with HFNC, infants who required intubation were initiated earlier in the course of their illness (3 days vs 4 days; p = 0.02). The ROX index did not demonstrate discriminatory ability at time of HFNC initiation (AUROC 0.6; p = 0.5) or 1 hour after initiation (AUROC 0.6; p = 0.6). Conclusions The ROX index at HFNC initiation and 1 hour did not predict early treatment failure in infants with bronchiolitis. Examination of a larger cohort of infants and greater number of treatment failures is required.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".