Institutional Care for Long‐Term Mechanical Ventilation in Canada: A National Survey
Bibliographic record
Abstract
INTRODUCTION: No national Canadian data define resource requirements and care delivery for ventilator-assisted individuals (VAIs) requiring long-term institutional care. Such data will assist in planning health care services to this population. OBJECTIVE: To describe institutional and patient characteristics, prevalence, equipment used, care elements and admission barriers for VAIs requiring long-term institutional care. METHODS: Centres were identified from a national inventory and snowball referrals. The survey weblink was provided from December 2012 to April 2013. Weekly reminders were sent for six weeks. RESULTS: The response rate was 84% (54 of 64), with 44 adult and 10 pediatric centres providing data for 428 VAIs (301 invasive ventilation; 127 noninvasive ventilation [NIV]), equivalent to 1.3 VAIs per 100,000 population. An additional 106 VAIs were on wait lists in 18 centres. More VAIs with progressive neuromuscular disease received invasive ventilation than NIV (P<0.001); more VAIs with chronic obstructive pulmonary disease (P<0.001), obesity hypoventilation syndrome (P<0.001) and central hypoventilation syndrome (P=0.02) required NIV. All centres used positive pressure ventilators, 21% diaphragmatic pacing, 15% negative pressure and 13% phrenic nerve stimulation. Most centres used lung volume recruitment (55%), manually (71%) and mechanically assisted cough (55%). Lack of beds and provincial funding were common admission barriers.CONCLUSIONS: Variable models and care practices exist for institutionalized care of Canadian VAIs. Patient prevalence was 1.3 per 100,000 Canadians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".