Assessing the Effect of Preoperative Nutrition on Upper Body Function in Elderly Patients Undergoing Elective Abdominal Surgery
Bibliographic record
Abstract
BACKGROUND: Malnutrition among elderly surgical patients has been associated with poor postoperative outcomes and reduced functional status. Although previous studies have shown that nutrition contributes to patient outcomes, its long-term impact on functional status requires better characterization. This study examines the effect of nutrition on postoperative upper body function over time in elderly patients undergoing elective surgery. METHODS: This is a 2-year prospective study of elderly patients (≥70 years) undergoing elective abdominal surgery. Preoperative nutrition status was determined with the Subjective Global Assessment (SGA). The primary outcome was handgrip strength (HGS) at 1, 4, 12, and 24 weeks postsurgery. Repeated measures analysis was used to determine whether SGA status affects the trajectory of postoperative HGS. RESULTS: . The median (interquartile range) Charlson Comorbidity Index was 3 (2-6). Participants were categorized as well-nourished (86%) and mildly to moderately malnourished (14%), with mean preoperative HGS of 25.8 ± 9.2 kg and 19.6 ± 7.0 kg, respectively. At 24 weeks, 64% of well-nourished patients had recovered to baseline HGS, compared with 44% of mildly to moderately malnourished patients. Controlling for relevant covariates, SGA did not significantly affect the trajectory of postoperative HGS. CONCLUSION: While HGS values over the 24 weeks were consistently higher in the well-nourished SGA group than the mildly to moderately malnourished SGA group, no difference in the trajectories of HGS was detected between the groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".