A Novel Remote Follow-Up Tool Based on an Instant Messaging/Social Media App for the Management of Patients With Low Anterior Resection Syndrome: Pilot Prospective Self-Control Study
Bibliographic record
Abstract
BACKGROUND: Low anterior resection syndrome (LARS) is a common functional disorder that develops after patients with rectal cancer undergo anal preservation surgery. Common approaches to assess the symptoms of patients with LARS are often complex and time-consuming. Instant messaging/social media has great application potential in LARS follow-up, but has been underdeveloped. OBJECTIVE: The aim of this study was to compare data between a novel instant messaging/social media follow-up system and a telephone interview in patients with LARS and to analyze the consistency of the instant messaging/social media platform. METHODS: Patients with R0 resectable rectal cancer who accepted several defecation function visits via the instant messaging/social media platform and agreed to a telephone interview after the operation using the same questionnaire including subjective questions and LARS scores were included. Differences between the 2 methods were analyzed in pairs and the diagnostic consistency of instant messaging/social media was calculated based on telephone interview results. RESULTS: In total, 21 questionnaires from 15 patients were included. The positive rates of defecation dissatisfaction, life restriction, and medication use were 10/21 (48%), 11/21 (52%), and 8/21 (38%) for telephone interview and 10/21 (48%), 13/21 (62%), and 5/21 (24%) for instant messaging/social media, respectively. No statistically significant difference was observed between instant messaging/social media and telephone interview in terms of total LARS score (mean 22.4 [SD 11.9] vs mean 24.7 [SD 10.7], P<.21) and LARS categories (Z=-0.264, P=.79); however, instant messaging/social media showed a more negative tendency. The kappa values of 3 subjective questions were 0.618, 0.430, and 0.674, respectively. The total LARS scores were consistent between both groups (Pearson coefficient 0.760, P<.001; category correlation coefficient 0.570, P=.005). Patients with major LARS had highly consistent results, with sensitivity, specificity, kappa value, and P value of 77.8%, 91.7%, 0.704, and .001, respectively. CONCLUSIONS: Instant messaging/social media can be a major LARS screening method. However, further research on information accuracy and user acceptance is needed before implementing a mature system. TRIAL REGISTRATION: ClinicalTrials.gov NCT03009747; https://clinicaltrials.gov/ct2/show/NCT03009747.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".