Implementation of telemedicine for knee osteoarthritis: study protocol for a randomized controlled trial
Bibliographic record
Abstract
Abstract Background Osteoarthritis (OA) is the most prevalent chronic joint disease, characterized by joint structural deterioration, pain and loss of function among the elders. It is also associated with several extra-articular symptoms (fatigue, sleep disorders, anxiety and depression) and a reduction of life quality. Studies have revealed that patients with OA benefitted from enhanced management via telemedicine. Guangdong Online Hospital (GOH) is the first officially recognized web-based hospital that provides telemedicine service in China. However, the effective implementation of GOH telemedicine (GOHT) to enhance management for patients with OA remains unknown. Methods/design An assessor-blinded, parallel randomized controlled trial will be performed to study the feasibility and effectiveness of GOHT in the enhanced management of OA. Forty participants with knee OA will be recruited for a 6-month study. Patients meeting the inclusion criteria will be randomly allocated to receive conventional therapy (CT) or conventional therapy plus a brief GOH-based intervention (CT-GOHT). The primary outcome is the feasibility of a full-scale randomized controlled trial. The secondary outcomes include the self-reported total score of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Multidimensional Fatigue Inventory (MFI), the Pittsburgh Sleep Quality Index (PSQI) and the Hospital Anxiety and Depression Scale (HADS). Assessments will be performed at baseline, 2 weeks, 3 months and 6 months later after the initiation of the study. Discussion This trial is intended to test the application of GOHT in the chronic management in knee OA. The hypothesis is that OA patients may receive disease management via this network platform conveniently and effectively, especially those in the remote areas of our country. GOHT telemedicine would be an attractive alternative to traditional methods for disease management in knee OA. The results could provide preliminary experiences and guidance for an upcoming full-scale randomized controlled trial (RCT) in disease management via telemedicine. Trial registration ChiCTR: ChiCTR1800014465 . Registered on 16 January 2018.
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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 | 0.039 | 0.034 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.072 | 0.009 |
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".