Describing taste changes and their potential impacts on paediatric patients receiving cancer treatments
Bibliographic record
Abstract
OBJECTIVES: Taste changes are common among paediatric patients receiving cancer treatments although specific descriptions and associations are uncertain. Primary objective was to describe the number of paediatric patients receiving cancer therapies who experienced taste changes, its impact on food intake and enjoyment of eating, and coping strategies. METHODS: This was a cross-sectional study that included English-speaking paediatric patients aged 4-18 years with a diagnosis of cancer or haematopoietic stem cell transplantation recipients receiving active treatment. Using a structured interview, we asked participants about their experience with taste changes, impacts and coping strategies. The respondent was the paediatric patient. RESULTS: We enrolled 108 patients; median age was 11 years (IQR 8-15). The taste changes reported yesterday or today were food tasting bland (34%), bad (31%), different (27%), bitter (25%), extreme (19%), metallic (15%) or sour (12%). Taste changes were associated with decreased food intake (31%) and decreased enjoyment in eating (25%) yesterday or today. The most common coping strategies were eating food they liked (42%), eating strong-tasting food (39%), drinking liquids (35%), brushing teeth (31%) and sucking on candy (25%). Factors significantly associated with food tasting bad were as follows: older age (p=0.003), shorter time since cancer diagnosis (p=0.027), nausea and vomiting (p=0.008) and mucositis (p=0.009). CONCLUSIONS: Among paediatric patients receiving cancer treatments, taste changes were common and were associated with decreased food intake and enjoyment in eating. Common coping strategies were described. Reducing nausea, vomiting and mucositis may improve taste changes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".