Clinical Prediction of Weaning and Extubation in Australian and New Zealand Intensive Care Units
Bibliographic record
Abstract
Our objective was to describe, in Australian and New Zealand adult intensive care units, the relative frequency in which various clinical criteria were used to predict weaning and extubation, and the weaning methods employed. Participant intensivists at 55 intensive care units completed a self-administered questionnaire, using visual analogue scales (0 = not at all predictive, 10 = perfectly predictive, not used = null score) to record the perceived utility of 30 potential predictors. Survey response rate was 71% (164/230). Those variables thought most predictive of weaning readiness were respiratory rate (median score 8.0, interquartile range 7.0 to 8.6) effective cough (7.3, 5.9 to 8.2) and pressure support setting (7.2, 6.0 to 8.0). The most highly rated predictors of extubation success were effective cough (8.0, 7.0 to 9.0), respiratory rate (8.0, 7.0 to 8.5) and Glasgow Coma Score (7.9, 6.1 to 8.3). Variables perceived least predictive of weaning and extubation success were P0.1, Acute Physiological and Chronic Health Evaluation score II, mean arterial pressure, electrolytes and maximum inspiratory pressure (individual median scores < 5). Most popular clinical criteria were those perceived to have high predictive accuracy, both for weaning (respiratory rate 96%, pressure support setting 94% and Glasgow coma score 91%) and extubation readiness (respiratory rate 98%, effective cough 94% and Glasgow Coma Score 92%). Weaning mostly employed pressure support ventilation (55%), with less use of synchronised intermittent mandatory ventilation (32%) and spontaneous breathing trials (13%). Classic ventilatory performance predictors including respiratory rate and effective cough were reported to be of greater clinical utility than other more recently proposed measures.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".