Bibliographic record
Abstract
PURPOSE OF REVIEW: As the discipline of supportive and palliative cancer care grows, there is increasing acknowledgement of taste and smell alterations (TSAs) as barriers to nutrient intake and detriments to the food-related quality of life and well being of the population. The focus of this brief review is to summarize the recent advances regarding the cause and nature of TSAs and patients' perceptions of TSAs and to identify promising approaches for the alleviation of TSAs. RECENT FINDINGS: Individual variations in the nature of TSAs exist and chemosensory changes have been established as an important quality of life issue associated with prognosis. The development and partial recovery of radiation therapy induced TSAs have been observed in longitudinal studies. Flavor enhancement has been found to improve patient-reported taste and smell capabilities. Patient-reported tools and qualitative methodologies have provided insight into the impact of TSAs on food-related quality of life and have been used with clinical measures to relate patient's perception to objective outcomes. SUMMARY: A variety of approaches to the assessment of TSAs continue to generate a description of the development, duration and recovery of distorted chemosensory perception in cancer patients. Attention to individual variation in the nature and severity of TSAs as well as nutritional support and focus on flavorful foods can enhance patients' well being and food-related quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".