Bibliographic record
Abstract
In preterm infants, re-intubation is common and associated with morbidity. Predicting extubation outcome remains a clinical challenge. Our objective was to develop two prediction models: 1. Re-intubation within 7 days, and 2. Non-invasive respiratory support (NRS) failure within 72 hours. We conducted a retrospective study in 25 Canadian neonatal intensive care units, over 2.5 years, involving infants born 230/7 - 286/7 weeks gestational age, extubated from invasive mechanical ventilation. Predictors were chosen based on clinical relevance. A two-level generalized estimating equations approach was used to account for clustering. Model performance was assessed through discrimination, calibration, goodness of fit, and internal validation. Of 1816 included infants, 20% required re-intubation, and 29% NRS failure. Both models had good performance but varying discrimination: re-intubation model AUC 0.704 (95% CI 0.673 – 0.735), NRS failure model AUC 0.658 (95% CI 0.620 – 0.676). In conclusion, our prediction models may help to refine future models with the aim of improving extubation outcomes.
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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 | 0.002 | 0.011 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".