Effectiveness of WeChat-Based Plus Scene-Graphics Health Education for Rehabilitation After Open Elbow Arthrolysis: Historical Control Study
Bibliographic record
Abstract
Background: Elbow stiffness often hinders daily tasks. Open elbow arthrolysis is effective but requires long-term postoperative rehabilitation. Traditional health education does not significantly improve patient cooperation or results. Handy and engaging tools such as WeChat and scene graphics may help. Objective: This study aims to assess the efficacy of WeChat-based health education combined with scene graphics following open elbow arthrolysis. Methods: This historical control study involved patients aged 18 years and older who underwent open elbow arthrolysis, had normal communication skills, and were proficient in using WeChat. Eligible patients were divided into 2 groups based on admission time: the control group (56 patients, enrolled from January to June 2021) and the WeChat group (56 patients, enrolled from July to December 2021). The control group had received traditional health education, whereas the WeChat group received health education using WeChat and scene graphics. Information in 4-part comics was shared through a WeChat public account. Patients accessed this account to receive daily lessons during hospitalization, followed by online instruction in a WeChat group after discharge until 12 weeks postoperatively. Outcome data were collected at 1, 6, and 12 weeks postoperatively. The primary outcome was elbow range of motion; secondary outcomes were elbow function, quality of life, and complication incidence. Results: The elbow flexion angle improved from 71.5° (SD 4.2°) to 124.2° (SD 11.7°) in the WeChat group and from 71.7° (SD 4.6°) to 114.4° (SD 13.6°) in the control group (difference 10.0°, 95% CI 4.9-15.1, P<.001). The mean elbow extension angle improved from 29.6° (SD 6.0°) to 6.4° (SD 2.5°) in the WeChat group and from 28.8° (SD 3.8°) to 10.1° (SD 3.4°) in the control group (difference -4.5°, 95% CI -6.5 to -2.5, P<.001). The mean forearm pronation angle improved from 31.9° (SD 4.0°) to 66.9° (SD 7.3°) in the WeChat group and from 33.0° (SD 4.2°) to 63.1° (SD 7.2°) in the control group (difference 4.9°, 95% CI 2.0-7.8, P=.001). The mean forearm supination angle improved from 30.2° (SD 3.7°) to 71.8° (SD 4.8°) in the WeChat group and from 30.4° (SD 4.1°) to 64.2° (SD 9.8°) in the control group (difference 7.7°, 95% CI 4.4-11.0, P<.001). The mean Mayo Elbow Performance Score increased from 58.0 (SD 3.7) to 80.4 (SD 5.7) in the WeChat group and from 58.9 (SD 2.8) to 75.8 (SD 6.9) in the control group (difference 5.5 points, 95% CI 2.8-8.2, P<.001). The mean 36-item Short Form Health Survey questionnaire score increased from 44.4 (SD 6.6) to 82.0 (SD 7.1) in the WeChat group and from 44.0 (SD 6.4) to 75.0 (SD 11.2) in the control group (difference 6.6 points, 95% CI 2.6-10.6, P=.002). Conclusions: WeChat-based health education combined with scene graphics was found to significantly improve elbow range of motion, elbow function, and quality of life.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".