Malnutrition risk, outcomes, and costs among older adults undergoing elective surgical procedures: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: We examine here the association between malnutrition risk and adverse health outcomes among older adult patients undergoing elective surgical procedures. METHODS: We conducted a retrospective study using linked clinical and administrative databases. Malnutrition risk was assessed prior to surgery, defined by unintentional weight loss and decreased food intake. We performed a logistic regression analysis of the primary outcome, a composite adverse outcome measure, including death, bleeding, pneumonia, and other surgical complications. We conducted Fine-Gray proportional hazard regression analysis of hospital length of stay (LOS). We performed a generalized linear regression analysis of in-hospital cost data. All regression analyses controlled for frailty, age, sex, surgical category, and comorbidities. RESULTS: Of a total of 3457 older adult elective surgical patients (65-102 years), 310 (9.0%) screened positive for malnutrition risk. In multivariable regression analyses, malnutrition risk was associated with an increased risk of the composite adverse outcome (odds ratio [OR] = 1.74; 95% CI = 1.25-2.39), higher hospitalization costs (relative cost = 1.84; 95% CI = 1.59-2.13), and a decreased risk of discharge from the hospital (hazard ratio = 0.67; 95% CI = 0.59-0.77) compared with those who screened negative. CONCLUSION: Older adult patients with malnutrition risk were at an increased risk of adverse surgical outcomes, had longer LOS in the hospital, and incurred higher costs of care. It is important to screen for malnutrition risk and refer older adults for dietetic consults prior to elective surgery.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".