Eliminating Digestive Irregularities Caused by Late Effects: A Pilot Study of an Innovative Culinary Nutrition Intervention for Reducing Gastrointestinal Toxicity in Gynecologic Cancer Patients Who Have Undergone Pelvic Radiotherapy
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Pelvic radiotherapy (RT) improves survival in gynecologic cancer patients but often results in gastrointestinal (GI) toxicity, affecting quality of life. Standard nutrition guidance lacks specificity for these survivors, complicating dietary choices. To address this gap, the EDIBLE intervention was developed to offer structured dietary self-management skills to alleviate RT-induced GI toxicity. METHODS: We conducted a single-arm mixed-methods pilot of the EDIBLE intervention among post-treatment gynecologic cancer survivors to assess its feasibility, acceptability, and preliminary effects on GI symptoms, knowledge, and self-efficacy, with measures at baseline (T1), post-intervention (T2), and after 3 months (T3). RESULTS: Qualitative interviews supported strong perceptions of intervention feasibility; however, the recruitment (32%) and retention (72%) rates were modest, indicating that alternate formats for program delivery may be needed to make it more accessible. The acceptability of the EDIBLE intervention garnered especially high ratings on measures of satisfaction and utility, with program improvements largely rallying around a desire for increased in-class sessions and program expansion. Statistically significant improvements were observed at the three-month mark (T3), such as enhanced confidence in culinary practices, increased knowledge and skills with regard to managing GI side effects, and improvements in bowel and GI symptoms. CONCLUSIONS: The results suggest EDIBLE is acceptable, improving GI symptoms and self-efficacy; however, moderate recruitment rates indicate refinement is needed. A randomized control trial and cost-effectiveness analysis is needed to confirm effectiveness and scalability.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".