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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".