Noninvasive Ventilation Practice Patterns for Acute Respiratory Failure in Canadian Tertiary Care Centres: A Descriptive Analysis
Bibliographic record
Abstract
BACKGROUND: The extent of noninvasive ventilation (NIV) use for patients with acute respiratory failure in Canadian hospitals, indications for use and associated outcomes are unknown. OBJECTIVE: To describe NIV practice variation in the acute setting. METHODS: A prospective observational study involving 11 Canadian tertiary care centres was performed. Data regarding NIV indication, mode and outcomes were collected for all adults (>16 years of age) treated with NIV for acute respiratory failure during a four-week period (between February and August 2011). Logistic regression with site as a random effect was used to examine the association between preselected predictors and mortality or intubation. RESULTS: A total of 330 patients (mean [± SD] 30±12 per centre) were included. The most common indications for NIV initiation were pulmonary edema (104 [31.5%]) and chronic obstructive pulmonary disease (99 [30.0%]). Significant differences in indications for NIV use across sites, specialty of ordering physician and location of NIV initiation were noted. Although intubation rates were not statistically different among sites (range 10.3% to 45.4%), mortality varied significantly (range 6.7% to 54.5%; P=0.006). In multivariate analysis, the most significant independent predictor of avoiding intubation was do-not-resuscitate status (OR 0.11 [95% CI 0.03 to 0.37]). CONCLUSION: Significant variability existed in NIV use and associated outcomes among Canadian tertiary care centres. Assignment of do-not-resuscitate status prevented intubation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".