Impact of a specialized multidisciplinary tracheostomy team on tracheostomy care in critically ill patients
Bibliographic record
Abstract
BACKGROUND: A multidisciplinary tracheostomy team was created in 2005 to follow critically ill patients who had undergone a tracheostomy until their discharge from hospital. Composed of a surgeon, surgical resident, respiratory therapist, speech-language pathologist and clinical nurse specialist, this team has been meeting twice a week for rounds involving patients who transitioned from the intensive care unit (ICU) to the medical and surgical wards. Our objective was to assess the impact of this multidisciplinary team on downsizing and decannulation times, on the incidence of speaking valve placement and on the incidence of tracheostomy-related complications on the ward. METHODS: This study was conducted at a tertiary care, level-1 trauma centre and teaching hospital and involved all patients who had received a tracheostomy during admission to the ICU from Jan. 1 to Dec. 31, 2004 (preservice group), and from Jan. 1 to Dec. 31, 2006 (postservice group). We compared the outcomes of patients who required tracheostomies in a 12-month period after the team was created with those of patients from a similar time frame before the establishment of the team. RESULTS: There were 32 patients in the preservice group and 54 patients in the postservice group. Under the new tracheostomy service, there was a decrease in incidence of tube blockage (5.5% v. 25.0%, p = 0.016) and calls for respiratory distress (16.7% v. 37.5%, p = 0.039) on the wards. A significantly larger proportion of patients also received speaking valves (67.4% v. 19.4%, p < 0.001) after creation of the team. Furthermore, there appeared to be a decreased time to first tube downsizing (26.0 to 9.4 d) and decreased time to decannulation (50.4 to 28.4 d), although this did not reach statistical significance owing to our small sample size. CONCLUSION: Standardized care provided by a specialized multidisciplinary tracheostomy team was associated with fewer tracheostomy-related complications and an increase in the use of a speaking valve.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".