An online educational and supportive care application for rectal cancer survivors with low anterior resection syndrome: A mixed methods pilot study
Bibliographic record
Abstract
AIM: Restorative proctectomy is commonly associated with significant bowel dysfunction, known as low anterior resection syndrome (LARS), which has a negative impact on patients' quality of life. We developed an online patient-centred application on LARS (eLARS) for rectal cancer survivors. The primary objective of this study was to assess the feasibility of eLARS for rectal cancer survivors with LARS following restorative proctectomy. The secondary objective was to explore participants' experiences with LARS and the eLARS application. METHODS: This was a mixed methods study, which included a feasibility and qualitative analysis. Participants were rectal cancer survivors who underwent restorative proctectomy for rectal cancer within 3 years, completed all adjuvant treatment, and suffered from bowel dysfunction postoperatively. Participants were given access to the application over a 2-month study period. Feasibility was defined as 75% of study participants using the application ≥4 times per month. Semi-structured interviews were conducted with participants after the study period and were analysed using thematic analysis. RESULTS: Our sample included eight rectal cancer survivors, five women and three men. The median age was 58.5 years (56.5-64.5). Most participants (75%) were >1-year post-restorative proctectomy. 75% of study participants used the application ≥4 times per month for 2 months. Our thematic analysis revealed that participants felt that they lacked access to credible information and emotional support around the time of ileostomy closure, and found that eLARS addressed these challenges. CONCLUSION: eLARS is a feasible educational and supportive care intervention for patients with LARS and has the potential to improve patients' 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".