Measurement Repeatability and Reproducibility of Virtual Goniometry of a Set of Acquired Images in Youths with Arthrogryposis Multiplex Congenita.
Bibliographic record
Abstract
OBJECTIVES: To evaluate the intra-rater repeatability and the inter-rater reproducibility of using a virtual goniometer to measure upper and lower extremity joint range of motion (ROM) in youths with arthrogryposis multiplex congenita (AMC). METHODS: Youths presenting with AMC aged 8 to 21 years old were recruited. ROM of the upper and lower limbs were assessed remotely during a teleassessment on a video-conferencing platform. Screen captures were taken and ROM were measured by two raters, two-weeks apart, using a virtual goniometer. Intraclass correlation coefficient (ICC) and associated 95% confidence interval (CI) were calculated to assess intra-and inter-rater repeatability and reproducibility. RESULTS: Nine participants were included with a median age of 15.9 years (range: 11.3 to 20.8 years). The overall intra-rater ICC was 0.997 (95% CI:0.996 to 0.997) for the first rater and 0.993 (95% CI:0.992 to 0.994) for the second rater. The inter-rater ICC ranged from 0.410 (95% CI:-0.392; 0.753) for forearm pronation to 0.998 (95% CI:0.996; 0.999) for elbow flexion. CONCLUSIONS: Results of the current study suggest that virtual goniometry is reproducible and repeatable for the ROM of most joints. Future studies should evaluate procedural reliability and validity of the proposed method for youth with complex conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".