Why We Should Look at Dinner Plates: Diet Changes in Cancer Patients
Bibliographic record
Abstract
OBJECTIVE: Malnutrition is often underestimated in the context of cancer therapy: the dietary trends initiated by patients after diagnosis are usually neither known to nor evaluated by the medical staff. Here, we propose a combined screening instrument evaluating malnutrition and dietary trends. METHODS: The validated screening tool NRS-2002 was combined with a four-item questionnaire assessing whether (1) patients preferred certain foods, (2) avoided certain foods, (3) used dietary supplements or followed a special diet since the time of cancer diagnosis. The screening tool was routinely used by cancer patients in the daily practice of three oncological departments. The presented analysis was performed retrospectively and anonymized. RESULTS: Overall, 102 cancer patients undergoing systemic therapy (CP), 97 undergoing radiation therapy (RP), and 36 head-neck cancer patients (HNP) were screened. The CP cohort showed a higher rate of malnutrition (50.00%) than the HNP (28.13%) or RP (26.80%) cohort. Overall, diet changes were observed in 33.63% of all patients. Avoiding meat, stimulants, or hard and edgy food was often mentioned in free text answers, while patients reported a preference for fruit and vegetables. Nutritional supplements were used by 28.76% of the patients. While dietary changes were common, only 6.64% of the patients mentioned adhering to a specific cancer diet. CONCLUSION: Malnutrition is still underestimated nowadays. Diet trends, especially avoiding certain foods, are common in cancer patients, while adhering to a specific cancer diet is an exception. Diet trends should be assessed and addressed to avoid or aggravate malnutrition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".