Noninvasive Ventilation Initiation in Clinical Practice: A Six‐Year Prospective, Observational Study
Bibliographic record
Abstract
BACKGROUND: Despite evidence supporting the role of noninvasive ventilation (NIV) in diverse populations, few publications describe how NIV is used in clinical practice. OBJECTIVE: To describe NIV initiation in a teaching hospital that has a guideline, and to characterize temporal changes in NIV initiation over time. METHODS: A prospective, observational study of continuous positive airway pressure ventilation (CPAP) or bilevel NIV initiation from January 2000 to December 2005 was conducted. Registered respiratory therapists completed a one-page data collection form at NIV initiation. RESULTS: Over a six-year period, NIV was initiated in 623 unique patients (531 bilevel NIV, 92 CPAP). Compared with bilevel NIV, CPAP was initiated more often using a nasal interface, with a machine owned by the patient, and for chronic conditions, especially obstructive sleep apnea. Whereas CPAP was frequently initiated and continued on the wards, bilevel NIV was most frequently initiated and continued in the emergency department, intensive care unit and the coronary care unit. Patients initiated on bilevel NIV were more likely to be female (OR 1.8, 95% CI 1.08 to 2.85; P=0.02) and to have an acute indication compared with CPAP initiations (OR 7.5, 95% CI 1.61 to 34.41; P=0.01). Bilevel NIV was initiated more often in the emergency department than in the intensive care unit (OR 5.8, 95% CI 0.89 to 38.17; P=0.07). Bilevel NIV initiation increased from 2000 to 2005. CONCLUSIONS: The present study illustrates how NIV is used in clinical practice and confirms that NIV initiation has increased over time.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".