Health Impact of Hospital Restrictions on Seriously Ill Hospitalized Patients
Bibliographic record
Abstract
BACKGROUND: Restrictions on non-urgent hospital care imposed to control the 2003 Toronto severe acute respiratory syndrome outbreak led to substantial disruptions in hospital clinical practice, admission, and transfer patterns. OBJECTIVES: We assessed whether there were unintended health consequences to seriously ill hospitalized patients. STUDY DESIGN, SETTING, AND POPULATION: Population-based longitudinal cohort study of patients residing in Toronto or an urban control region with an incident admission for 1 of 7 serious conditions in the 3 years before, or the 4 months during or after restrictions. OUTCOME MEASURES: Short-term mortality, overall readmissions, cardiac readmissions for acute myocardial infarction patients, serious complications for very low birth weight babies, and quality of care measures, comparing adjusted rates across time periods within regions. RESULTS: Mortality, readmission, and complication rates did not change for any condition during or after severe acute respiratory syndrome restrictions. Although rates of invasive cardiac procedures for acute myocardial infarction patients decreased 11-37% in Toronto, rates of nonfatal cardiac outcomes did not change. CONCLUSIONS: Restrictions on non-urgent hospital utilization and hospital transfers may be a safe public health strategy to employ to control nosocomial outbreaks or provide hospital surge capacity for up to several months, in large, well-developed healthcare systems with good availability of community-based care.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".