Telemedicine-based measurement of finger joint range of motion in patients: A reliability and concurrent validity study
Bibliographic record
Abstract
BACKGROUND: Although the use of telemedicine has persisted in hand surgery and therapy practices beyond the COVID-19 pandemic, there remains a need for simple, validated means of remotely measuring finger joint range of motion for integration in fast-paced virtual clinics. We propose on-screen measurement, a technique previously validated in the elbow, which involves holding a goniometer up to the telemedicine appointment screen. PURPOSE: This study aimed to determine the reliability and concurrent validity of on-screen measurements relative to the gold standard, in-person goniometry. Congruence of management plans established at virtual and in-person appointments was as a secondary aim. STUDY DESIGN: Prospective Reliability and Agreement (Concurrent Validity) Study. METHODS: Patients with Dupuytren's disease assessed virtually and in-person were recruited from one surgeon's practice. Virtual and in-person measurements in maximal passive extension, time between appointments and treatment plans made at each visit were extracted from patients' charts. In-person assessors were blinded to previous telemedicine-based measurements and, after a 2-week washout period, the original assessor and two additional assessors re-measured joints from screenshots captured at telemedicine appointment. Descriptive and statistical analyses were used to evaluate inter-rater and intra-rater reliability as well as concurrent validity. RESULTS: Fifty-four eligible patients (191 joints; 102 digits) attended telemedicine and in-person appointments at a median of 31 days apart. Inter-rater and intra-rater reliability were excellent (intraclass correlation coefficient >0.96). The absolute mean difference between on-screen and in-person measurements was 8˚, with 61.7% of on-screen measurements falling within 10˚ of in-person measurements. Management plans made at the telemedicine appointment were congruent with those carried out in-person in 96.3% of cases. CONCLUSIONS: On-screen measurement is highly reliable with concurrent validity that compares to similar photography-based measurement studies. Our results suggest on-screen measurement may be a useful tool for initial consultation and triaging of patients with flexion contractures.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".