The Association between Malnutrition and Psychological Distress in Patients with Advanced Head-and-Neck Cancer
Bibliographic record
Abstract
OBJECTIVE: Malnutrition and psychological distress are often seen in patients with head-and-neck cancer, but little is known about the interrelationships between those two symptoms. The present study examined the relationship between malnutrition and psychological distress in patients with advanced head-and-neck cancer. METHODS: Using the Patient-Generated Subjective Global Assessment, 99 patients with advanced-stage head-and-neck cancer were screened for nutrition status. The patients were also screened for psychosocial distress (using the Distress Thermometer) and for psychosocial issues (using the Problem Checklist). Any relationship between malnutrition and psychosocial distress was determined by regression and correlation analysis. We also used t-tests to compare distress levels for patients with and without specific nutrition-related symptoms. RESULTS: The study group included 80 men and 19 women [mean age: 58.4 ± 10.9 years (range: 23-85 years)]. The correlation between poorer nutrition status and level of psychological distress was significant r = 0.37 (p < 0.001). Specifically, reduced food intake and symptoms were both positively associated with distress: r = 0.27 and r = 0.29 respectively, both significant at p < 0.01. After controlling for the effects of psychosocial problems and pain, nutrition status remained a significant predictor of distress, explaining 3.8% of the variance in the distress scores of the patients (p < 0.05). CONCLUSIONS: Malnutrition and symptoms were strongly related to distress in patients with advanced head-and-neck cancer. Our results suggest the need for further research into the complex relationship between nutrition status and distress and into the management of both nutrition and distress in cancer care.
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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 | 0.003 |
| 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 | 0.001 |
| 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".