Accelerating Post-Surgical Best Practices Using Enhanced Recovery After Surgery
Bibliographic record
Abstract
Patients undergoing surgery today experience longer hospital stays and more complications because evidence-based practices in the areas of nutrition, activity, opioid-sparing analgesia, hydration and overall best practices are not consistently applied or used. There is also emerging evidence that supporting patients and families to become engaged in their perioperative care improves outcomes. Enhanced Recovery After Surgery (ERAS) helps patients be more prepared for surgery and recover more quickly by bringing patients, healthcare providers and health systems together and creating tools and resources that are based on the most up-to-date evidence. The goal of Enhanced Recovery Canada is to support the uptake of these best practices across Canada, improving patient outcomes and experiences. M rs. Lee awaits colon cancer surgery. Her healthcare/surgery team works with her to identify her concerns and to tailor support through evidencebased pathways to help her prepare mentally and physically (e.g., optimizing her diet, activity and medical conditions), which help ease her worry. She uses a customized tablet-based Enhanced Recovery app to track her symptoms and to know when to eat and drink at all times on her surgical journey. After surgery, she knows what to expect and is ready to move and eat the very same day. She has less nausea and pain than she expected. Mrs. Lee is discharged from hospital only 4 days post-surgery. She continues to use her Enhanced Recovery app and has regular follow-ups, which alleviate her anxiety. Six weeks later, she says she feels ready to start chemotherapy. The surgery team tracks her symptoms, uses appropriate pain and symptom control to optimize her recovery and encourages her to move, avoiding the muscle wasting, weakness and frailty that are a consequence of immobilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".