Using simulation for training and to change protocol during the outbreak of severe acute respiratory syndrome
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
INTRODUCTION: During the 2003 severe acute respiratory syndrome (SARS) crisis, we proposed and tested a new protocol for cardiac arrest in a patient with SARS. The protocol was rapidly and effectively instituted by teamwork training using high-fidelity simulation. METHODS: Phase 1 was a curriculum design of a SARS-specific cardiac arrest protocol in three steps: planning the new protocol, repeated simulations of this protocol in a classroom, and a subsequent simulation of a cardiac arrest on a hospital ward. Phase 2 was the training of 275 healthcare workers (HCWs) using the new protocol. Training involved a seminar, practice in wearing the mandatory personal protection system (PPS), and cardiac arrest simulations with subsequent debriefing. RESULTS: Simulation provided insights that had not been considered in earlier phases of development. For example, a single person can don a PPS worn for the SARS patient in 1 1/2 minutes. However, when multiple members of a cardiac arrest team were dressing simultaneously, the time to don the PPS increased to between 3 1/2 and 5 1/2 minutes. Errors in infection control as well as in medical management of advanced cardiac life support (ACLS) were corrected. CONCLUSION: During the SARS crisis, real-time use of a high-fidelity simulator allowed the training of 275 HCWs in 2 weeks, with debriefing and error management. HCWs were required to manage the SARS cardiac arrest wearing unfamiliar equipment and following a modified ACLS protocol. The insight gained from this experience will be valuable for future infectious disease challenges in critical care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it