Radiotherapy for the prophylaxis of heterotopic ossification: A systematic review and meta-analysis of published data
Bibliographic record
Abstract
INTRODUCTION: Following surgery, the formation of heterotopic ossification (HTO) can limit mobility and impair quality of life. Radiotherapy has been proven to provide efficacious prophylaxis against HTO, especially in high-risk settings. PURPOSE: The current review aims to determine the factors influencing HTO formation in patients receiving prophylactic radiotherapy. METHODS: A systematic search of the literature was conducted on Ovid Medline, Embase and the Cochrane Central Register of Controlled Trials. Studies were included if they reported the percentage of sites developing heterotopic ossification after receiving a specified dose of prophylactic radiotherapy. Weighted linear regression analysis was conducted for continuous or categorical predictors. RESULTS: Extracted from 61 articles, a total of 5464 treatment sites were included, spanning 85 separate study arms. Most sites were from the hip (97.7%), from United States patients (55.2%), and had radiation prescribed postoperatively (61.6%) at a dose of 700cGy (61.0%). After adjusting for radiation site, there was no statistically significant relationship between the percentage of sites developing HTO and radiation dose (p=0.1) or whether radiation was administered preoperatively or postoperatively (p=0.1). Sites with previous HTO formation were more likely to develop recurrent HTO than those without previous HTO formation (p=0.04). There was a statistically significant negative relationship between the HTO development and the cohort mean year of treatment (p=0.007). CONCLUSION: Decreases in rates of HTO over time in this patient population may be a function of more efficacious surgical regimens and prophylactic radiotherapy.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".