Potential pediatric intensive care unit demand/capacity mismatch due to novel pH1N1 in Canada
Bibliographic record
Abstract
OBJECTIVE: To investigate the possibility of pediatric intensive care unit shortfalls, using pandemic models for a range of attack rates and durations. The emergence of the swine origin pH1N1 virus has led to concerns about shortfalls in our ability to provide pediatric ventilation and critical care support. DESIGN: Modeling of pediatric intensive care demand based on pH1N1 predictions using simulation techniques. SETTING: Simulation laboratory. PATIENTS: None. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data collected during the first wave of the pH1N1 in children in Canada were applied to several second wave pandemic models to explore potential pediatric intensive care unit ventilatory demands for Canada and to investigate the impact of vaccination upon these demands. In almost all cases studied, even for relatively low attack rates of 15%, significant pediatric intensive care unit shortages would be expected to occur. Vaccination strategies targeting 50% of the population significantly reduced demand, but shortages may still be expected. Although shortfalls can occur in all provinces, Ontario and British Columbia may experience the greatest supply-demand difference, even at low attack rates. CONCLUSION: Reducing the attack rate among children, whether through vaccination or additional measures, such as social distancing, will be critical to ensure sufficient pediatric intensive care unit capacity for continued pediatric care.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".