Driving Perioperative Nutrition Quality Improvement Processes Forward!
Bibliographic record
Abstract
Evidence supporting the important role of nutrition therapy in surgical patients has evolved, with several randomized trials and meta-analyses of randomized trials clearly demonstrating benefits. Despite this evidence, surgeons and anesthesiologists have been slow to adopt recommended practices, and the traditional dogma of delaying the initiation of and restricting the amount of nutrition during the postoperative period persists. Consequently, the nutrition therapy received by surgical patients remains suboptimal; thus, patients suffer worse clinical outcomes. Knowledge translation (KT) describes the process of moving evidence learned from clinical research, and summarized in clinical practice guidelines, to its incorporation into clinical and policy decision making. In this paper, we apply Graham et al's knowledge-to-action model to illuminate our understanding of the issues pertinent to KT in surgical nutrition. We illustrate various components of this model using empirically derived research, commentaries, and published studies from both critical care and surgical nutrition. Barriers to improving surgical nutrition practice may be related to (1) the nature of the underlying evidence and clinical practice guidelines; (2) guideline implementation factors; (3) characteristics of the health system, hospital, and surgical team; (4) provider attitudes and beliefs; and (5) patient factors (eg, type of surgery, underlying disease, and nutrition status). Interventions tailored to overcoming these barriers must be developed, evaluated, and implemented. A system of audit and feedback must guide this process and evaluate improvements over time so that every patient undergoing major surgery will have the opportunity to be optimally assessed and managed according to best nutrition practices.
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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 | 0.121 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".