Three-dimensional remodelling to determine best fit for hemihamate autograft arthroplasty
Bibliographic record
Abstract
Objective To determine the best fit of a hamate osteochondral graft to reconstruct a palmar defect of a middle phalanx articular fracture using three-dimensional remodelling. Methods The proximal middle phalanx and distal hamate articular surfaces of 10 cadaveric right hands were scanned using a three-dimensional laser scanner. A defect was marked on the middle phalanx digital image to simulate a 50% palmar lip fracture. A hemihamate autograft surgical procedure was simulated by aligning the middle phalanx and hamate digital articular surfaces. In addition to the second digit measurements, the midpoint distances of the central ridge of proximal articular surface of the middle phalanx digital image for digits 3, 4 and 5 were recorded for reference value, as well as the offset distances for the long and small finger. Results The mean midpoint distance for the index finger was 2.96 mm (95% CI 2.71 mm to 3.21 mm). The mean angle of offset was 20.09° (95% CI 15.54° to 24.64°). The mean graft offset distance was prominent by 1.23 mm (95% CI 0.57 mm to 1.89 mm). The reference values for the third, fourth and fifth middle phalange midpoint distances were 3.26 mm (95% CI 3.09 mm to 3.43 mm), 3.13 mm (95% CI 2.93 mm to 3.33 mm) and 2.48 mm (95% CI 2.33 mm to 2.63 mm), respectively. The offset distances for digits 3 and 5 were 1.24 mm (95% CI 0.48 mm to 2.00 mm) and 1.08 mm (95% CI 0.48 mm to 1.68 mm), respectively. Conclusions The present study provides information about best fit for placing a hamate autograft for the hemihamate arthroplasty procedure. In this model, the hamate graft must be offset to recreate the curvature of the middle phalanx.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".