Prevalence of Malnutrition among Cancer Patients in a Nigerian Institution
Bibliographic record
Abstract
Background: Cancer is a major health problem. Successful management includes adequate supportive care. Nutritional problems are common among cancer patients and these are not routinely addressed by oncologists during oncology care leading to suboptimal outcome even in developed countries. In Nigeria and other low and medium income countries, the situation is worse as nutritional screening and assessment of cancer patients are not routinely carried out. Objectives: To determine the proportion of cancer patients at risk of malnutrition and compare convergence of risk assessment using SGA and MUST tools. Methods: This was a prospective study carried out among cancer patients who presented for cancer care in the Department of Radiation Oncology, University College Hospital Ibadan, Nigeria. Nutritional assessment tools which included Malnutrition Universal Scoring Tool (MUST) and Subjective Global Assessment (SGA) were used to assess the nutritional status of the participants. Results: A total of 89 patients aged between 18 and 85 years participated in the study. The number of males were 13 (15%) while females were 76(85%). In our study 54 (60.8%) of our patients were at risk of malnutrition using the malnutrition universal scoring tool (MUST) scale while 53(60%) were malnourished using the subjective global assessment (SGA) scale. The reliability for the classifications using the MUST and SGA scales was positive (moderate) [Kappa = 0.584 (p<0.0005), 95% CI (0.410, 0.758)]. Conclusion: There is a high proportion of clinical malnutrition among cancer patients in the study population. According to this study, there was similarity between the classifications of nutritional risk, using the MUST and SGA tools.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".