Development of a Patient-Based Goniometric System for the Assessment of Contracture Conditions in Dupuytren’s Disease
Bibliographic record
Abstract
BACKGROUND: Treatment outcomes of Dupuytren's disease depend largely on degree of contracture and biological severity. Longitudinal assessment of each is crucial for effective care and long-term outcome assessment. Ideally, each Dupuytren's patient should have ongoing interval evaluations. Because of the large number of Dupuytren's patients, it would be impractical and costly for health care professionals to examine every patient in person on a regular basis. Patient-based evaluations might provide a useful and cost-effective alternative to office-based examination. METHODS: Finger goniometry is the standard metric for office-based evaluation of Dupuytren's disease. This study's goal was to develop a new patient-reported goniometric system. The authors developed a completely Web-based goniometric software for patients to use without supervision and without undue effort or cost. They then evaluated the validity and precision of the core measurement system and the reliability of its patient-based application. RESULTS: With a correlation of 0.992 (p < 0.01), a mean deviation of -0.25 degree, and a standard deviation of 2.74 degrees in patient-based application, the authors found their goniometric software to be comparable to practitioner-based, conventional goniometry. The authors believe patient-based goniometry to be a sufficiently accurate, valid, and reliable approach for longitudinal clinical assessment of Dupuytren's disease. CONCLUSIONS: Patient-based goniometric approaches have great potential for inexpensive, accurate, and accessible longitudinal assessment of the large population of Dupuytren's patients. Such approaches could help to substantially improve overall care of Dupuytren's disease through early diagnosis and timely treatment. In addition, being able to collect reliable patient data on a regular basis and on a larger scale could help improve understanding of the natural history of Dupuytren's disease. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, I.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".