The provision of critical care in emergency departments at Canada
Bibliographic record
Abstract
INTRODUCTION: Critically ill patients are common in emergency medicine, and require expert care to maximize patient outcomes. However, little data is available on the provision of critical care in the ED. The goal of this study is to describe the management of critically ill patients in the ED via a survey of Canadian emergency physicians. MATERIALS AND METHODS: A survey of attending physician members of CAEP was conducted by email. The survey was developed by the authors and internal validity was established prior to survey deployment. Data on physician demographics, hospital resources, use of invasive procedures, vasopressor/inotropic medications, length of stay in the ED and patient responsibility were assessed. RESULTS: The survey response rate was 22.9%, with the majority of respondents possessing speciality training in EM (73.5%). Respondents indicated that critically ill patients were commonly managed in the ED, with 68.5% reporting >6 critically ill patients per month, and 12.4% indicating > 20 patients per month. Respondents indicated that the majority of critically ill patients remained in the ED for 1-4 hours (70%) after resuscitation, yet 18% remained in the ED for >5 hours. Patients with a "respiratory" etiology were the most common critically ill patient population reported, followed by "cardiovascular", "infectious" and "traumatic illness". Direct laryngoscopy was frequently performed (66.9%> 11 in the year prior to the survey) in the year prior to the survey, while other invasive procedures and vasopressor/inotropic medications were utilized less often. EM physicians were responsible for the management of critically ill patients in the ED, even after consultation to an inpatient service, and were often required to provided acute care to critically ill patients admitted to an ICU, yet remaining in the ED prior to transfer (20% reported > 50% of the time). CONCLUSION: Our survey demonstrates that critically ill patients are common in Canadian ED's, and that EMP's are often responsible to provide care for prolonged period of time. In addition, the use of invasive procedures other then direct laryngoscopy was variable. Further research is warranted to determine the impact of delayed transfer and ED physician management of critically ill patients in the ED.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".