Commentary Communication in the Toronto critical care community: important lessons learned during SARS
Bibliographic record
Abstract
The SARS outbreak in 2003 pushed Toronto’s health care system to its limits. Staffing shortages, transmission of SARS within the ICU, and the influx of critically ill SARS patients were some unique challenges to the delivery of critical care. Communication strategies were a key component in the critical care response to SARS. Regular teleconference calls, web-based training and education, and the rapid coordination of research studies were some of the initiatives developed within the Toronto critical care community during the SARS outbreak. Other critical care communities should consider their communication strategies in advance of similar events. Keywords communication, critical care, disease outbreaks, SARS In the spring of 2003, Toronto found itself in the midst of a worldwide outbreak of SARS. The Toronto outbreak followed a biphasic course lasting from 5 March to 12 June. A total of 375 probable and suspect cases of SARS (as defined using World Health Organization criteria [1]) were reported in Ontario, of which 44 died [2]. The vast majority of these
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.030 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".