Bibliographic record
Abstract
BACKGROUND: The Greater Toronto Area (GTA) was considered a "hot zone" for severe acute respiratory syndrome (SARS) in 2003. In accordance with mandated city-wide infection control measures, the Hospital for Sick Children (HSC) drastically reduced all services while maintaining a fully operational emergency department. Because of the GTA health service suspensions and the overlap of SARS-like symptoms with many common childhood illnesses, this introduced the potential for a change in the volumes of patients visiting the emergency department of the only regional tertiary care children's hospital. METHODS: We compared HSC emergency department patient volumes, admission rates and length of stay in the emergency department in the baseline years of 2000-2002 (non-SARS years) with those in 2003 (SARS year). The data from the prior years were modeled as a time series. Using an interrupted time series analysis, we compared the 2003 data for the periods before, during and after the SARS periods with the modeled data for significant differences in the 3 aforementioned outcomes of interest. RESULTS: Compared with the 2000-2002 data, we found no differences in visits, admission rates or length of stay in the pre-SARS period in 2003. There were significant decreases in visits and length of stay (p < 0.001) and increases in admission rates (p < 0.001) during the periods in 2003 when there were new and active cases of SARS in the GTA. All 3 outcomes returned to expected estimates coincident with the absence of SARS cases from September to December 2003. INTERPRETATION: During the SARS outbreak in the GTA, the HSC emergency department experienced significantly reduced volumes of patients with low-acuity complaints. This gives insight into utilization rates of a pediatric emergency department during a time when there was additional perceived risk in using emergency department services and provides a foundation for emergency department preparedness policies for SARS-like public health emergencies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".