Next-Generation Remote Hand Assessments: Cross-Platform DIGITS Web Application
Bibliographic record
Abstract
Purpose: We have previously developed DIGITS, a platform for remote evaluation of range of motion, dexterity, and swelling of fingers for reducing barriers to accessing clinical resources. The current study was aimed at evaluating DIGITS across different devices with varied operating systems and camera resolutions using a single person's hands. Methods: Our team has now developed a web application version of the DIGITS platform, which makes it accessible on any device that is equipped with a camera, including computers, tablets, and smartphones. In the present study, we aimed to validate this web application by comparing flexion and extension measurements on the same person's hands using three different devices with cameras of different resolutions. The absolute difference, SD, standard mean error, and intraclass correlation coefficient were calculated. Additionally, equivalency testing was performed using the confidence interval approach. Results: Our findings indicated that the differences in degree measured between the devices ranged from 2° to 3° when digit extension was assessed (all hand landmarks are visible in the camera's direct view) and from 3° to 8° when digit flexion was assessed (some of the hand landmarks are hidden from view). The intraclass correlation coefficient of individual trials ranged from 0.82 to 0.96 for extension and 0.77 to 0.87 for flexion across all devices. Additionally, within a 90% confidence interval, our data showed equivalency with measurements using three different devices. Conclusions: The absolute differences were within an acceptable 9° tolerance for measurements taken between devices for flexion and extension. Equivalency was observed for measurements of finger range of motion taken using DIGITS, regardless of devices, platforms, or camera resolutions. Clinical relevance: In summary, the DIGITS web application has good test-retest reliability to generate data on finger range of motion for hand telerehabilitation. DIGITS can reduce costs to patients, providers, and health care facilities for conducting postoperative follow-up assessments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".