Development of an <i>In Vitro</i> Swan Neck Deformity Biomechanical Model
Bibliographic record
Abstract
Background: Injury to the finger’s extensor mechanism is a common cause of swan neck deformity (SND). Progression of extensor and flexor tendon imbalance negatively affects laxity of the volar plate, resulting in the inhibition of proper finger motion. The complexity of finger anatomy, however, makes understanding the pathomechanics of these deformities challenging. Therefore, development of an SND model is imperative to understand its influence on finger biomechanics and to provide an in vitro model to evaluate the various treatment options. Methods: The index, middle, and ring fingers from 8 cadaveric specimens were used in an in vitro active motion simulator to replicate finger flexion/extension. An SND model was developed through sectioning of the terminal extensor tendon at the distal insertion (creating a mallet finger) and transverse retinacular ligament (TRL). A strain gauge inserted under the volar plate measured laxity of the plate, and electromagnetic trackers recorded proximal interphalangeal joint (PIPJ) angles. Results: Strain in the volar plate increased progressively with creation of the mallet and SND conditions ( P = .015). Although not statistically significant, the mallet finger condition accounted for 26% of the increase, whereas sectioning of the TRL accounted for 74% ( P = .031). As predicted, PIPJ hyperextension was not detectable by joint angle measurement; however, the PIPJ angle had a strong positive correlation with volar plate strain ( R 2 = 1.0, P < .001). Conclusion: Volar plate strain measurement, in an in vitro model, can detect an induced SND. Moreover, as a surrogate for PIPJ hyperextension, volar plate strain may be useful to evaluate the time-zero effectiveness of various surgical interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".