Bilateral triceps rupture: a review of the literature and case series
Bibliographic record
Abstract
Introduction Distal triceps tendon injuries are the least common of tendon injuries, comprising 1% all cases. This injury is more common in active men, between 30-50 years old. Risk factors include anabolic steroid use, local steroid injections and hyperparathyroidism. The principal mechanism of injury is an eccentric load applied to a contracting triceps. This injury is often misdiagnosed and thus far, bilateral injury has not been reported. Methods Retrospective description of two patients diagnosed with bilateral distal triceps injury with different presentations, treated surgically at a surgical center in the USA. Electronic records were reviewed for patient demographics, rehabilitation and clinical outcomes. Discussion The triceps tendon may be weakened by stem cell or corticoid injections or may cause tendinosis. Early diagnosis and surgical repair with a Krackow-type suture configuration in the triceps and parallel bone tunneling is a safe technique with a secure repair. Graded rehabilitation can offer patients good outcomes for range of motion and strength. Clinicians can explain that patients can expect to return to sport and activity. Conclusion Triceps tendon rupture is a rare, significant injury that requires a secure surgical repair with bone tunnelling. Our experience careful post-operative rehabilitation with gradual motion and strengthening is the key for successful recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".