Study design to determine the effects of widespread restrictions on hospital utilization to control an outbreak of SARS in Toronto, Canada
Bibliographic record
Abstract
CONTEXT: Efforts to control an outbreak of severe acute respiratory syndrome (SARS) in Toronto, Canada, led to the imposition of major restrictions on nonurgent use of hospital-based services. OBJECTIVE: To describe a methodology to determine the impact of the restrictions on healthcare utilization. DESIGN, SETTING, POPULATION: Population-based study of the Greater Toronto area and unaffected comparator regions, before, during and after the SARS outbreak (April 2001 to March 2004). OUTCOME MEASURES: Population-based rates of hospital admissions, emergency department and outpatient visits, inter-hospital transfers, diagnostic testing and essential drug prescribing, adjusted for age and sex. METHODS: We will assess the temporal patterns of healthcare utilization rates before, during and after the SARS restrictions in different regions using administrative health databases and longitudinal data analysis methods (generalized estimating equations). We will also use longitudinal cohort models to assess the effects of the restrictions to outcomes in cohorts diagnosed with specific chronic diseases. CONCLUSION: We will document the extent to which utilization of healthcare services decreased during the SARS epidemic and identify clinical problem areas where SARS-related restrictions had adverse consequences on health. This work will have planning implications for future outbreaks of SARS or other infectious diseases. Understanding how the outbreak control measures affected use of health services and ultimately the health of the population, is an important part of understanding the impact of SARS restrictions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".