Heterotopic Ossification following Total Elbow Arthroplasty in a Patient with Parkinson’s Disease: Case Report and Literature Review
Bibliographic record
Abstract
Introduction. Heterotopic ossification (HO) usually develops following surgery or trauma. Risk factors for HO following elbow fractures include delay to surgery (>7 days), floating fractures, and elbow subluxation. Systemic risk factors for HO include male sex; concurrent cranial, neurological, or abdominal injury; high-energy trauma; previous development of HO; and contralateral fracture. To date, no studies have reported on Parkinson’s disease (PD) as a risk factor for the development of HO.Case Presentation. A 68-year-old female with PD (treated with levodopa-carbidopa) sustained a right closed (OTA type A3) distal humerus fracture and was treated with a total elbow arthroplasty. Postoperatively, development of significant near-ankylosing HO was observed and contributed to significant restriction of elbow motion with activities of daily living. After HO maturation, the osseous growth was excised, and the area irradiated. The patient regained excellent elbow motion with no recurrence of HO.Discussion. A literature review revealed six cases of HO development in PD patients following arthroplasty. Patients with PD have higher serum concentrations of interleukins (IL) and tumor necrosis factor- (TNF-)α. These factors stimulate BMP-2 production which may promote osteogenesis. Levodopa-carbidopa may also influence HO through stimulation of growth hormone and IGF-1.Conclusion. Parkinsonism may promote heterotopic bone growth through the release of osteoinductive factors. HO development may also be mediated by levodopa-carbidopa therapy. Future research should highlight the link between HO and PD and identify if prophylaxis is warranted in PD patients undergoing arthroplasty.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".