Agreement on clinical examination and management between virtual videoconferencing and in-person assessment in a tertiary shoulder center
Bibliographic record
Abstract
Background: This study examined the intra and inter-examiner agreement of clinical tests and a management plan between a virtual video encounter and an in-person assessment in patients seen at a specialty shoulder program at an academic center. Methods: Participants were evaluated using a web-based video conferencing program and an in-person assessment. Guidelines on proper positioning of the camera and specific tests were outlined and sent in advance to all patients. Range of motion (ROM) was documented. Shoulder-specific clinical signs examined rotator cuff and proximal biceps tendon. Management was categorized as nonoperative or operative. Intraclass correlation coefficients and Kappa coefficients (κ) were used. Results: Thirty patients, 14 (47%) females, mean age: 57.5 (SD = 11), were examined twice. The intraclass correlation coefficients values ranged from 0.83 to 0.87 for shoulder ROM, indicating good reliability. Cervical spine Spurling test had an almost perfect agreement (κ = 0.83). The κ values were substantial for the hornblower sign (κ = 0.78), painful arc (κ = 0.65), lift-off test (κ = 0.65), and belly press test (κ = 0.61). Agreement was moderate for the cervical spine ROM (κ = 0.60), Jobe's test (κ = 0.57), Hawkins-Kennedy (κ = 0.52), and cross-body adduction (κ = 0.47). The management plan showed almost perfect agreement with respect to conservative vs. surgical management (κ = 0.83). Percentage of agreement for inter-examiner agreement ranged from 0.80 to 1.00. Conclusions: The majority of shoulder clinical tests for rotator cuff pathology conducted via a structured video encounter were shown to be reliable. The resulting proposed management plans were essentially identical.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".