A North American Survey of Respiratory Therapist and Physician Tracheostomy Decannulation Practices
Bibliographic record
Abstract
BACKGROUND: Tracheostomy is a common surgical procedure performed on critically ill patients. However, little is known about how clinicians make decisions to decannulate patients, and whether similar decisions are made by respiratory therapists (RTs) and physicians. METHODS: We performed a cross-sectional survey of RTs (n = 52) and physicians (n = 102) at 54 medical centers in North America, to characterize contemporary decannulation practices. RESULTS: RTs and physicians rated ability to tolerate capping, secretions, cough effectiveness, and level of consciousness as the most important factors in the decannulation decision, with RTs placing greater emphasis on ability to tolerate capping and physicians on level of consciousness. In the clinical scenarios, RTs and physicians recommended decannulation with similar frequency (52% vs 55%, P = .54). Patients were most likely to be recommended for decannulation if they had a strong cough, scant thin secretions, required minimal supplemental oxygen, and were alert and interactive. In addition, RTs were more likely to recommend decannulation for patients who demonstrated an ability to tolerate tracheostomy tube capping for 72 hours and whose etiology of respiratory failure was chronic obstructive pulmonary disease. RTs preferred shorter time frames for defining decannulation failure than did physicians (median response 48 h vs 96 h, P = .02 for test of proportions). Both groups identified 2-5% (median response) as an acceptable rate of decannulation failure (P = .48 for test of proportions). CONCLUSIONS: Important differences exist in the decannulation practices of North American RTs and physicians. Evidence-based tracheostomy guidelines are needed to facilitate the safe and effective management of patients with tracheostomies.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".