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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".