A comparative cadaveric biomechanical analysis of the differences between dynamic external traction devices for PIP joint fracture dislocation.
Bibliographic record
Abstract
Purpose: No study in the literature compares different external distractors for PIPJ injury. We compared a device described by Suzuki et al and another by Hynes & Giddins in non-injured cadaveric fingers. Main outcome measures were articular space and PIPJ flexion resistance.Methods: Thirty-two Thiel embalmed fingers were used. The elastics based model was performed with 3 and 5 elastics per side (3E and 5E); the 2-pin model used no elastics (2P). Articular distraction of each device was measured using x-ray imaging. The force required to flex the PIP joint to 45˚ and 90˚ in each group was measured with a dynamometer.Main findings: The articular distraction was statistically significant for all groups. The difference in articular distraction was significant in the AP view between groups 3E and 2P, and 5E and 2P. Flexion forces were only significant between group 5E and 2P at 90˚ flexion, but resistance was notably higher in group 2P than in groups 3E and 5E. Group 2P was more difficult to engage and often disengaged in flexion compared to groups 3E and 5E.Conclusion: All devices achieved significant articular distraction (>99% in AP) but optimal distraction has not been clinically determined and may depend on each unique fracture, hence a variable distraction device may be optimal. The 3E and 5E models can be adjusted for distraction by adding the sufficient elastics to reduce individual fractures. The increased resistance to PIP flexion found in the 2P model may limit post-op mobilization, but clinical correlation is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".