Aplicación del programa ERAS® como una política de salud pública en el sistema de salud de Alberta, Canadá
Bibliographic record
Abstract
Enhanced Recovery After Surgery (ERAS®) was implemented across Alberta Health Services, a single payer publicly funded provincial health system starting in 2013. Implementation across multiple provincial sites in colorectal surgery reduced postoperative complications by 12% and median length of stay by one day. Subsequent implementation in gynecologic oncology reduced postoperative complications by 17% and length of stay by 2 days in high complexity surgery. Implementation has had an estimated net savings in the province of $7.22 million Canadian dollars (CAD) over 5 years with a return on investment of $1.05 to $7.31 for every dollar invested in the project. Patient involvement enabled success of the program, with support, education, and mitigation of patient stress identified as key components for success. Provider knowledge and motivation were essential to ensure ongoing compliance with ERAS guidelines. Provider education, and demonstration of improvement in patient outcomes using audit is one method to ensure continued motivation from care providers. Systemlevel leadership is essential to provide consistent messaging and support for initiatives, while providerlevel leadership in the form of physician champions and nurse coordinators ensures compliance and appropriate integration of ERAS into daily practice. Implementation of ERAS across a unified health care system has improved patient outcomes while saving resources. Further research into expansion of the program to community hospitals and all surgical domains is underway.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".