Tracheostomy Decannulation and Cough Peak Flows in Patients with Neuromuscular Weakness
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the relationship between cough peak flows (CPFs) before and after tracheostomy tube removal (decannulation) in patients with neuromuscular respiratory muscle weakness. DESIGN: For 26 patients with occluded tracheostomies (capped or Passy-Muir valve), spontaneous CPF (CPF(sp)), CPF after lung volume recruitment (CPF(LVR)), and CPF after lung volume recruitment and a manually assisted cough (CPF(LVR) + MAC) were measured before and after decannulation. RESULTS: Decannulation resulted in a significant increase (P < 0.001) in CPF of 35.6, 34.5, and 42.6 l/min for CPF(sp), CPF(LVR), and CPF(LVR) + MAC, respectively. In addition, CPF(LVR) or CPF(LVR) + MAC with a capped tracheostomy in place were greater than spontaneous CPF with the tracheostomy tube removed. CONCLUSIONS: Our study suggests that assisted coughing with a capped tracheostomy tube in place can result in higher flows than removing the tube and relying on spontaneous cough alone. Postdecannulation CPF measured at the mouth can be predicted to be at least 34.5 l/min greater than predecannulation values, which may thereby lower the threshold of the CPF indicated for safe decannulation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".