Interactive online informational and peer support application for patients with low anterior resection syndrome: patient survey and protocol for a multicentre randomized controlled trial
Bibliographic record
Abstract
AIM: Low anterior resection syndrome (LARS) refers to a constellation of bowel symptoms that affect the majority of patients following restorative proctectomy. LARS is associated with poorer quality of life (QoL), and can lead to distress, anxiety and isolation. Peer support could be an important resource for people living with LARS, helping them normalize and validate their experience. The aim of this work is to describe the development of an interactive online informational and peer support app for LARS and the protocol for a randomized controlled trial. METHOD: A multicentre, randomized, assessor-blind, parallel-groups pragmatic trial will involve patients from five large colorectal surgery practices across Canada. The trial will evaluate the impact of an interactive online informational and peer support app for LARS, consisting of LARS informational modules and a closed forum for peers and trained peer support mentors, on patient-reported outcomes of people living with LARS. The primary outcome will be global QoL at 6 months following app exposure. The treatment effect on global QoL will be modelled using generalized estimating equations. Secondary outcomes will include patient activation and bowel function as measured by LARS scores. RESULTS: In order to better understand patients' interest and preferences for an online peer support intervention for LARS, we conducted a single institution cross-sectional survey study of rectal cancer survivors. In total, 35/69 (51%) participants reported interest in online peer support for LARS. Age <65 years (OR 9.1; 95% CI 2.3-50) and minor/major LARS (OR 20; 95% CI 4.2-100) were significant predictors of interest in LARS online peer support. CONCLUSION: There is significant interest in the use of online peer support for LARS among younger patients and those with significant bowel dysfunction. Based on results of the needs assessment study, the app content and features were modified reflect patients' needs and preferences. We are now in an optimal position to rigorously test the potential effects of this initiative on patient-centered outcomes using a randomized controlled trial.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".