Preliminary Evaluation of a New Index to Predict the Outcome of a Spontaneous Breathing Trial
Bibliographic record
Abstract
BACKGROUND: The available predictors of spontaneous-breathing-trial (SBT) success/failure lack accuracy. We devised a new index, the CORE index (compliance, oxygenation, respiration, and effort). OBJECTIVE: To compare the CORE index to the CROP index (compliance, rate, oxygenation, and pressure), airway-occlusion pressure 0.1 s after the start of inspiratory flow (P(0.1)), and rapid shallow breathing index (RSBI) for predicting SBT success/failure in a critical care environment. METHODS: With 47 mechanically ventilated patients recovering from respiratory failure, of various causes, we prospectively examined the SBT success/failure prediction accuracy and calculated receiver operating characteristic curves, sensitivity, specificity, and likelihood ratios of CORE, CROP, P(0.1), and RSBI. RESULTS: The specificities were CORE 0.95, P(0.1) 0.70, CROP 0.70, and RSBI 0.65. The sensitivities were CORE 1.00, CROP 1.00, P(0.1) 0.93, and RSBI 0.89. The areas under the receiver operating characteristic curve were CORE 1.00 (95% CI 0.92-1.00), CROP 0.91 (95% CI 0.79-0.97), P(0.1) 0.81 (95% CI 0.67-0.91), and RSBI 0.77 (95% CI 0.62-0.88). The positive likelihood ratios were CORE 20.0, CROP 3.3, P(0.1) 3.1, and RSBI 2.5. The negative likelihood ratios were CORE 0.0, CROP 0.0, P(0.1) 0.1, and RSBI 0.2. CONCLUSIONS: The CORE index was the most accurate predictor of SBT success/failure.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".