GI factors, potential to predict prostate motion during radiotherapy; a scoping review
Bibliographic record
Abstract
Purpose: A scoping literature review was conducted to identify gastrointestinal (GI) factors most likely to influence prostate motion during radiotherapy. We proffer that patient specific measurement of these GI factors could predict motion uncertainty during radiotherapy, facilitating personalised care by optimising treatment technique e.g., daily adaption or via bespoke patient pre-habilitation and preparation. Methods: The scoping review was undertaken as per JBI guidelines. Searches were conducted across four databases: Ovid Medline®, EMBASE, CINAHL and EBSCO discovery. Articles written in English from 2010-present were included. Those pertaining to paediatrics, biological women exclusively, infectious and post-treatment GI morbidity and diet were excluded.Common GI factors impacting men were identified and related symptoms, incidence and measurement tools examined. Prevalence among persons with prostate cancer was explored and suitable assessment tools discussed. Results: A preliminary search identified four prominent GI-factors: mental health, co-morbidity and medication, physical activity, and pelvic floor disorder. The scoping search found 3644 articles; 1646 were removed as duplicates. A further 1249 were excluded after title and abstract screening, 162 remained subsequent to full text review: 42 mental health, 53 co-morbidity and medication, 39 physical activity and 28 pelvic floor disorder.Six GI factors prevalent in the prostate cancer population and estimated most likely to influence prostate motion were identified: depression, anxiety, diabetes, obesity, low physical activity, and pelvic floor disorder. Reliable, quick, and easy to use tools are available to quantify these factors. Conclusion: A comprehensive GI factor assessment package suitable to implement into the radiotherapy clinic has been created. Unveiling these GI factors upfront will guide improved personalisation of radiotherapy.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".