Home Mechanical Ventilation: A 12-Year Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Increasing numbers of individuals are being initiated on home mechanical ventilation, including noninvasive (bi-level) and invasive mechanical ventilation delivered via tracheostomy due to chronic respiratory failure to enable symptom management and promote quality of life. Given the high care needs of these individuals, a better understanding of the indications for home mechanical ventilation, and health-care utilization is needed. METHODS: We performed a retrospective cohort study using provincial health administrative data from Ontario, Canada (population ∼13,000,000). Home mechanical ventilation users were characterized using health administrative data to determine the indications for home mechanical ventilation, the need for acute care at the time of ventilation approval, and their health service use and mortality rates following approval. RESULTS: The annual incidence of home mechanical ventilation approval rose from 1.8/100,000 in 2000 to 5.0/100,000 in 2012, or an annual increase of approximately 0.3/100,000 persons/y. The leading indications were neuromuscular disease, thoracic restriction, and COPD. The indication for the remainder could not be determined due to limitations of the administrative databases. Of the 4,670 individuals, 23.0% commenced home mechanical ventilation following an acute care hospitalization. Among individuals who survived at least 1 y, fewer required hospitalization in the year that followed home mechanical ventilation approval (29.9% vs 39.8%) as compared with the year prior. CONCLUSIONS: Utilization of home mechanical ventilation is increasing in Ontario, Canada, and further study is needed to clarify the factors contributing to this and to further optimize utilization of health-care resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".