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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".