Adherence to nutritional interventions in head and neck cancer patients: a systematic scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Dietary counselling provided by a dietitian, with or without oral nutritional supplements, can impact on nutritional and clinical outcomes in head and neck cancer (HNC) patients undergoing radiotherapy. However, little is known about the role of adherence to oral nutritional interventions in this population. This review aimed to map the literature for evidence of adherence to oral nutritional interventions in HNC patients undergoing radiotherapy and to identify gaps in knowledge in this field. METHODS: A scoping review methodology was used to identify studies, extract data, and collate and summarise results. We searched Medline, Embase, Cochrane Central and CINAHL, from the earliest available time up to 8 January 2020. RESULTS: In total, 2315 unique articles were identified, 163 studies were assessed in full and niner were included in the scoping review. The use of different measures to assess adherence and variability in the timing of the assessments was noted across studies. Despite identifying studies that have measured adherence to oral nutritional interventions, very few studies monitored its influence on clinical and nutritional outcomes in HNC patients or reported factors related to adherence. CONCLUSIONS: A robust evidence base is lacking for adherence to oral nutritional intervention in HNC patients. Overall, further studies evaluating the impact of oral nutritional interventions in HNC patients undergoing radiotherapy should measure adherence to the intervention. Early recognition of non-adherence and the contributing factors could ensure intensification of nutritional support and better health outcomes.
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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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".