Tutorial: Development and Implementation of a Multidisciplinary Preoperative Nutrition Optimization Clinic
Bibliographic record
Abstract
Although much is known about surgical risk, little evidence exists regarding how best to proactively address preoperative risk factors to improve surgical outcomes. Preoperative malnutrition is a widely prevalent and modifiable risk factor in patients undergoing surgery. Malnutrition prior to surgery portends significantly higher postoperative mortality, morbidity, length of stay, readmission rates, and hospital costs. Unfortunately, perioperative malnutrition is poorly screened for and remains largely unrecognized and undertreated-a true "silent epidemic" in surgical care. To better address this silent epidemic of surgical nutrition risk, here we describe the rationalization, development, and implementation of a multidisciplinary, registered dietitian-driven, preoperative nutrition optimization clinic program designed to improve perioperative outcomes and reduce cost. Implementation of this novel Perioperative Enhancement Team (POET) Nutrition Clinic required a collaboration among many disciplines, as well as an identified need for multidimensional scheduling template development, data tracking systems, dashboard development, and integration of electronic health records. A structured malnutrition risk score (Perioperative Nutrition Screen score) was developed and is being validated. A structured malnutrition pathway was developed and is under study. Finally, the POET Nutrition Clinic has established a novel role for a perioperative registered dietitian as the integral point person to deliver perioperative nutrition care. We hope this structured model of perioperative nutrition assessment and optimization will allow for wide implementation and generalizability in other centers worldwide to improve recognition and treatment of perioperative nutrition risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".