Clinical review: SARS - lessons in disaster management.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Disaster management plans have traditionally been required to manage major traumatic events that create a large number of victims. Infectious diseases, whether they be natural (e.g. SARS [severe acute respiratory syndrome] and influenza) or the result of bioterrorism, have the potential to create a large influx of critically ill into our already strained hospital systems. With proper planning, hospitals, health care workers and our health care systems can be better prepared to deal with such an eventuality. This review explores the Toronto critical care experience of coping in the SARS outbreak disaster. Our health care system and, in particular, our critical care system were unprepared for this event, and as a result the impact that SARS had was worse than it could have been. Nonetheless, we were able to organize a response rapidly during the outbreak. By describing our successes and failures, we hope to help others to learn and avoid the problems we encountered as they develop their own disaster management plans in anticipation of similar future situations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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 it