The Effect of Triceps Repair Techniques Following Olecranon Excision on Elbow Stability and Extension Strength: An In Vitro Biomechanical Study
Bibliographic record
Abstract
OBJECTIVES: To determine the effect of two types of triceps repair techniques on elbow stability and extension strength in the setting of olecranon deficiency using a cadaveric model. METHODS: Eight fresh-frozen cadaveric arms were tested in an elbow motion simulator, which produced active elbow extension by applying physiological loads to the tendons. Computed tomography-based surface models were used to determine cutting planes corresponding to sequential levels of olecranon resection. Both anterior and posterior triceps repairs were simulated for each level. RESULTS: Progressive sectioning of the olecranon increased elbow laxity for both active and passive extension (P < 0.001). Although the posterior repair resulted in greater laxity than the anterior repair for all but the 50% resection, this difference was small (less than 3°) and not statistically significant for either active (P = 0.2) or passive (P = 0.1) extension. Active extension produced less joint laxity than passive extension for both the anterior (P = 0.007) and posterior (P = 0.001) repairs. The posterior repair provided greater extension strength than the anterior repair at all applied triceps tensions and for all olecranon resections (P = 0.01). Both repairs reduced extension strength relative to the intact state (P < 0.01). Sequential olecranon excision decreased extension strength (P = 0.04); however, there were no differences between resection levels (P > 0.05). CONCLUSIONS: On average, there was a loss of extension strength of 24% and 30% for the posterior and anterior repairs, respectively. There was no significant difference in stability between repair techniques. Posterior repair of the triceps after olecranon excision would thus appear to be efficacious as a result of its higher triceps extension strength. However, clinical studies are needed to confirm these in vitro observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".