Development of a Universal Nutritional Screening Platform for Plastic Surgery Patients
Bibliographic record
Abstract
Plastic surgeons routinely see patients with complex or chronic wounds of all etiology. In a previous study, we found that up to 1 in 4 of these patients is at risk for malnutrition, which may be influencing their ability to heal. The goal of this study was to develop and validate a universal screening protocol that would be fast and accurate and allow for effective intervention and optimization of nutrition before plastic surgery. METHODS: To accomplish these goals, we adopted a 2-part screening algorithm using the Canadian Nutritional Screening Tool (CNST) to triage patients in our outpatient clinics and then further screened those identified as being at risk using the Subjective Global Assessment (SGA) tool and blood work. RESULTS: We screened 111 patients with diagnoses related to breast cancer (n = 10; 9.01%), elective surgery (n = 38; 34.23%), emergency surgery (n = 8; 7.21%), fractures (n = 15; 13.51%), and wounds (n = 40; 36.04%). Of the screened subjects, 15.32% (n = 17) were found to be at nutritional risk using the CNST, and 13 were confirmed to be moderately or severely malnourished using the SGA. Importantly, there were no positive correlations between nutritional status and smoking, diabetes, body mass index, or age, indicating that a universal screening protocol is needed to effectively screen a diverse plastic surgery population for malnutrition. CONCLUSIONS: Screening patients with both the CNST and the SGA is an effective way to identify patients before surgery to improve outcomes.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".