Cough peak flow to predict the extubation outcome: Comparison between three cough stimulation methods
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to compare the predictive value of three cough peak flow (CPF) maneuvers in predicting the extubation outcome in a cohort of mechanically ventilated subjects. METHODS: Eighty-one mechanically ventilated subjects who succeeded in the spontaneous breathing trial were included. In a randomized order, CPF was stimulated and measured using three methods: voluntary command (V_CPF), tracheal saline instillation (S_CPF), and mechanical stimulation with a catheter (C_CPF). Additionally, CPF was measured 20 min after the extubation (PE_CPF). The diagnostic accuracy of the CPF methods in relation to the extubation outcome was measured using the receiver operating characteristic (ROC) curve. ROC curve results were compared using the Hanley and McNeil method. RESULTS: = 0.13). The optimum cutoff values were V_CPF = 45 L/min, S_CPF = 60 L/min, and C_CPF = 55 L/min. PE_CPF also showed high accuracy in predicting the extubation outcome (AUC = 0.95; cutoff = 75 L/min). CONCLUSIONS: In mechanically ventilated and cooperative subjects, there is no difference in the accuracy of CPF measured voluntarily, with stimulation using saline or by catheter stimulation in predicting the reintubation. CPF recording after endotracheal tube removal has high accuracy to predict the extubation outcome.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".