The Perioperative Surgical Home, Enhanced Recovery After Surgery and how integration of these models may improve care for medically complex patients
Bibliographic record
Abstract
Perioperative medicine is changing rapidly, and with this change comes the opportunity to improve upon current models of care delivery and integration within the health care system. Perioperative models of care are structured or conceptual arrangements for surgical patients before, during and after their surgery. Models of care such as the Perioperative Surgical Home and Enhanced Recovery After Surgery pathways are increasingly used to guide the structure of perioperative care delivery with an aim to improve patient outcomes and experience in Canadian settings. In this narrative review, we summarize the origins of these perioperative models of care. They are fundamentally different in scope and level of evidence. Both models have potential benefits and limitations to their broad implementation in our health care system. As currently developed, both models are limited in their application to patients with chronic disease. We discuss how these models of care can be used to develop integrated horizontal and vertical perioperative pathways in a Canadian setting. Such integration is a potential solution that will improve their applicability to patients with medically complex conditions and in times when health care systems are under pressure. We describe this approach using the example of patients with kidney failure receiving dialysis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".