Association Between Nutritional Status and Survival in Patients Requiring Treatment for Spinal Metastases
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The Patient-Generated Subjective Global Assessment (PG-SGA) is a standardized tool for assessing malnutrition in patients with cancer. The primary aim of this study was to assess the impact of preoperative nutritional status as measured by PG-SGA on survival in patients requiring surgical intervention and/or radiotherapy for spinal metastases. METHODS: Patients with spinal metastases who underwent surgery and/or radiation therapy for symptomatic spinal metastases were enrolled in the AO Spine Metastatic Tumor Research and Outcomes Network, a prospective international multicenter research registry, between September 2017 and August 2022. Using the PG-SGA, nutritional status was classified into 3 categories: A, well nourished; B, moderately malnourished; and C, severely malnourished. RESULTS: A total of 589 patients met the inclusion criteria; 362 were classified as well nourished (61%), 159 were moderately malnourished (27%), and 68 were severely malnourished (12%). The median survival was 491 days, 328 days, and 117 days for well-nourished, moderately malnourished, and severely malnourished patients, respectively. In the multivariate analyses, severe malnourishment (HR 2.5 95% CI 1.4-4.3, P < .01) and an ECOG performance status of 3 or 4 (HR 2.7 95% CI 1.2-6.0) remained associated with significantly worse survival. CONCLUSION: Malnutrition as measured by the PG-SGA demonstrated to be significantly and independently associated with postoperative survival. The PG-SGA is a simple and useful tool to identify spinal metastases patients at risk of early postoperative mortality, and inclusion in the preoperative evaluation of these patients should be considered.
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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 | 0.000 | 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".