Agreement and concurrent validity between telehealth and in-person diagnosis of musculoskeletal conditions: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To assess the concurrent validity and inter-rater agreement of the diagnosis of musculoskeletal (MSK) conditions using synchronous telehealth compared to standard in-person clinical diagnosis. METHODS: We searched five electronic databases for cross-sectional studies published in English in peer-reviewed journals from inception to 28 September 2023. We included studies of participants presenting to a healthcare provider with an undiagnosed MSK complaint. Eligible studies were critically appraised using the QUADAS-2 and QAREL criteria. Studies rated as overall low risk of bias were synthesized descriptively following best-evidence synthesis principles. RESULTS: We retrieved 6835 records and 16 full-text articles. Nine studies and 321 patients were included. Participants had MSK conditions involving the shoulder, elbow, low back, knee, lower limb, ankle, and multiple conditions. Comparing telehealth versus in-person clinical assessments, inter-rater agreement ranged from 40.7% agreement for people with shoulder pain to 100% agreement for people with lower limb MSK disorders. Concurrent validity ranged from 36% agreement for people with elbow pain to 95.1% agreement for people with lower limb MSK conditions. DISCUSSION: In cases when access to in-person care is constrained, our study implies that telehealth might be a feasible approach for the diagnosis of MSK conditions. These conclusions are based on small cross-sectional studies carried out by similar research teams with similar participant demographics. Additional research is required to improve the diagnostic precision of telehealth evaluations across a larger range of patient groups, MSK conditions, and diagnostic accuracy statistics.
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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 | 0.104 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".