Critical care capacity in Canada: results of a national cross-sectional study
Bibliographic record
Abstract
Abstract Introduction Intensive Care Units (ICUs) provide life-supporting treatment; however, resources are limited, so demand may exceed supply in the event of pandemics, environmental disasters, or in the context of an aging population. We hypothesized that comprehensive national data on ICU resources would permit a better understanding of regional differences in system capacity. Methods After the 2009–2010 Influenza A (H1N1) pandemic, the Canadian Critical Care Trials Group surveyed all acute care hospitals in Canada to assess ICU capacity. Using a structured survey tool administered to physicians, respiratory therapists and nurses, we determined the number of ICU beds, ventilators, and the ability to provide specialized support for respiratory failure. Results We identified 286 hospitals with 3170 ICU beds and 4982 mechanical ventilators for critically ill patients. Twenty-two hospitals had an ICU that routinely cared for children; 15 had dedicated pediatric ICUs. Per 100,000 population, there was substantial variability in provincial capacity, with a mean of 0.9 hospitals with ICUs (provincial range 0.4-2.8), 10 ICU beds capable of providing mechanical ventilation (provincial range 6–19), and 15 invasive mechanical ventilators (provincial range 10–24). There was only moderate correlation between ventilation capacity and population size (coefficient of determination (R2) = 0.771). Conclusion ICU resources vary widely across Canadian provinces, and during times of increased demand, may result in geographic differences in the ability to care for critically ill patients. These results highlight the need to evolve inter-jurisdictional resource sharing during periods of substantial increase in demand, and provide background data for the development of appropriate critical care capacity benchmarks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".