Loss of Paramedic Availability in an Urban Emergency Medical Services System during a Severe Acute Respiratory Syndrome Outbreak
Bibliographic record
Abstract
Objectives: To describe the loss of paramedic availability to Toronto Emergency Medical Services during a biphasic (SARS-1 and SARS-2) outbreak of severe acute respiratory syndrome (SARS). Methods:During the SARS outbreak, a dedicated paramedic surveillance and quarantine program was developed. The authors determined the number of paramedics on quarantine each day, the type of quarantine (either home quarantine [HQ] or work quarantine [WQ]), and the development of SARS-like symptoms. Results: During the SARS outbreak, there were five cases of probable SARS and three cases of suspect SARS. SARS-1 lasted 30 days, during which 234 paramedics were placed on HQ. The total number of HQ days was 1,615. During the five peak days of SARS-1, the total number of HQ days was 664. SARS-2 lasted 18 days, during which 292 paramedics were placed on either HQ or WQ, for a combined number of quarantine days of 1,637. During the five peak days of SARS-2, the combined number of quarantine days was 910. Of these, paramedics were available for duty on 708 days (78%) due to the WQ program. The primary reason for quarantine was unprotected exposure to a health care institution experiencing a SARS outbreak. Under quarantine, SARS-like symptoms developed in 68 paramedics, including cough (53 [78%]), myalgia (33 [48%]), fatigue (30 [44%]), headache (29 [43%]), fever (11 [16%]), and shortness of breath (7 [10%]). Conclusions: Paramedics were among the health care workers who developed SARS. During SARS-2, WQ optimized the number of days on which paramedics were available for duty. Many paramedics developed SARS-like symptoms without being diagnosed as having SARS. A dedicated paramedic surveillance and quarantine program provided a useful means to manage the paramedic resource during the SARS outbreak.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".