Visual Estimation of Dupuytren’s Flexion Contractures—A Prospective Comparative Trial
Bibliographic record
Abstract
PURPOSE: Surgeons and resident physicians in a clinic setting often visually estimate Dupuytren flexion contractures of the hand to follow disease progression and decide on management. No previous study has compared visual estimates with a standardized instrument to ensure measurement reliability. METHODS: Consecutive patients consulted for Dupuytren flexion contractures of the hand had individual joint contractures estimated in degrees (°) by both a resident physician and staff surgeon. Estimates were compared with goniometer measurements to generate intraclass correlation coefficients (ICCs), and residents and surgeons were compared based on their accuracy. RESULTS: Twenty-eight patients enrolled in this study, which provided a total of 80 hand joints for analysis. Resident physicians achieved an ICC of 0.42, which indicates poor reliability. The hand surgeon achieved an ICC of 0.86, which indicates high reliability. The surgeon also had better accuracy than the residents. CONCLUSION: Hand surgeons should be mindful of the limitations of visual estimates of Dupuytren flexion contractures, particularly when conducted by trainees. Joint angle measurements taken for the purposes of research should be done with a goniometer at minimum.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.004 | 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".