Nutritional Status and Body Composition of Adult Patients with Brain Tumours Awaiting Surgical Resection
Bibliographic record
Abstract
PURPOSE: To measure the prevalence of malnutrition, risk factors for poor dietary intake and body composition in patients with brain tumours admitted to hospital for surgical resection. METHODS: In this study, 316 patients admitted for brain tumour resection to the Neurosurgical service at St. Michael's Hospital were screened. Assessment tools included the Subjective Global Assessment (SGA) for nutritional status and Bioelectrical Impedance Analysis (BIA) for body composition. All measurements were performed by one research dietitian. Information regarding medical history, symptomology, and tumour pathology was recorded. RESULTS: One hundred and nine participants were recruited. Malnutrition was present in 17.6% of patients, of whom 94.7% were moderately malnourished (SGA-B) and 5.3% severely malnourished (SGA-C). Key symptoms contributing to malnutrition included weight loss, nausea, vomiting, dysphagia, headaches, and fatigue. Patients with malignant tumors were more likely to have weight loss and lower fat mass. CONCLUSIONS: This study demonstrated that patients admitted for brain tumour resection have a low prevalence of malnutrition compared with other cancer populations. Useful parameters for nutritional screening of inpatient admissions include weight loss >5% of usual weight, nausea, vomiting, dysphagia, and headaches.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".