Investigating the Use of Telemedicine by Health Care Providers to Diagnose and Manage Patients With Musculoskeletal Disorders: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Access to care is a major challenge for patients with musculoskeletal disorders (MSKDs). Telemedicine is one of the solutions to improve access to care. However, initial remote diagnosis of MSKDs involves some challenges, such as the impossibility of touching the patient during the physical examination, which makes it more complex to obtain a valid diagnosis. No meta-analysis has been performed to date to synthesize evidence regarding the initial assessment including a physical evaluation using telemedicine to diagnose patients with MSKDs. OBJECTIVE: This study aims to appraise the evidence on diagnostic and treatment plan concordance between remote assessment using synchronous or asynchronous forms of telemedicine and usual in-person assessment for the initial evaluation of various MSKDs. METHODS: An electronic search was conducted up to August 2023 using terms related to telemedicine and assessment of MSKDs. Methodological quality of studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Random-effect model meta-analyses were performed. The Grading of Recommendations, Assessment, Development, and Evaluations framework was used to synthesize the quality and certainty of the evidence. RESULTS: A total of 23 concordance studies were eligible and included adult participants (N=1493) with various MSKDs. On the basis of high certainty, pooled κ and prevalence-adjusted and bias-adjusted κ for the diagnostic concordance between remote and in-person assessments of MSKDs were 0.80 (95% CI 0.72-0.89; 7 studies, 353 patients) and 0.83 (95% CI 0.76-0.89; 6 studies, 306 patients). On the basis of moderate certainty, pooled Gwet AC1 for treatment plan concordance between remote and in-person assessments of MSKDs was 0.90 (95% CI 0.80-0.99; 2 studies, 142 patients). CONCLUSIONS: The diagnostic concordance for MSKDs is good to very good. Treatment plan concordance is probably good to excellent. Studies evaluating the accuracy to detect red and yellow flags as well as the potential increase in associated health care resources use, such as imaging tests, are needed.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".