Characterizing Physician-Staffing Models in the Care of Postoperative Cardiac Surgical Patients in Canada
Bibliographic record
Abstract
BACKGROUND: Current intensive care unit physician-staffing (IPS) models for postoperative cardiac surgery have not been previously investigated in Canada. The purpose of this study was to determine current IPS models at 2 time points and describe the evolution of Canadian cardiac surgery IPS models. METHODS: A survey of 32 Canadian cardiovascular intensive care units (CVICUs) was undertaken in 2012 and 2017 to determine IPS models of care during "daytime" and "after-hours" in each unit. Data were collected regarding surgical volume, base specialties, and style of IPS management ("open"; "semi-open"; "closed"). In addition, we collected the overnight experience level of the bedside healthcare provider for in-house intensive care units. RESULTS: Survey responses were received from 27 of 32 CVICUs (87%). As of 2017, the style of 1 (4%) was open, 7 (26%) were semi-open, and 19 (70%) were closed in their unit IPS strategy. Base specialties of CVICU physicians varied. A medical doctor provided after-hours coverage in 81% of CVICUs. Senior residents (37%) or critical care certified attending staff (25%) typically provided after-hours coverage for in-house CVICUs. Linked Canadian Institute for Health Information data did not indicate a difference among CVICU models in mortality or rehospitalization for coronary artery bypass graft or valve procedures. CONCLUSIONS: Considerable heterogeneity is demonstrated in CVICU staffing patterns. No consensus was identified regarding the appropriate level of training for "after-hours" coverage. In-house overnight physician staffing in CVICUs varies widely. Finally, semi-open and closed style models did not demonstrate differences compared to Canadian Institute for Health Information data. Variability among CVICUs does exist; however, benefits of one model over another have not been identified.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".