Are Pathologic Fractures in Patients With Osteosarcoma Associated With Worse Survival Outcomes? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pathologic fractures occur in 5% to 10% of patients with osteosarcoma, and prior studies have suggested they are prognostically important. However, because they represent an uncommon event in the setting of an already rare disease, most studies fail to reach conclusive findings, and there is no agreement about how best to treat pathologic fractures. QUESTIONS/PURPOSES: (1) Is the occurrence of a pathologic fracture in patients with osteosarcoma associated with poorer overall survivorship? (2) Is the occurrence of a pathologic fracture in patients with osteosarcoma associated with poorer local recurrence-free survival or metastasis-free survival? (3) Is the surgical approach (amputation or limb salvage) associated with differences in local recurrence rates in patients with osteosarcoma with pathologic fractures? METHODS: This systematic review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our study was registered in PROSPERO (ID: 380459). A search of the PubMed and Embase databases resulted in 625 and 747 titles, respectively. After application of the inclusion and exclusion criteria, 21 articles were finally included. Quality assessment of all studies was performed using the Newcastle-Ottawa Quality Assessment Scale. The Risk of Bias In Non-Randomized Studies of Interventions tool was used in the 11 articles that evaluated the effect of an intervention (amputation or limb salvage) on local recurrence rates. The relative risk (RR) was calculated to compare outcomes in patients with osteosarcoma with pathologic fractures and those without. Heterogeneity among studies was calculated using the I 2 statistic. The pooled RR was calculated using the fixed-effects or random-effects model depending on study heterogeneity. The fragility index and the ratio between the fragility index and the total number of participants for each outcome was additionally calculated to assess the robustness of our results. A total of 7604 patients with osteosarcoma, 12% of whom (885) had pathologic fractures, were included in our analysis. RESULTS: Pathologic fractures in patients with osteosarcoma were associated with lower 3-year (RR 1.53 [95% CI 1.29 to 1.82]; p < 0.001) and 5-year overall survival (RR 1.27 [95% CI 1.16 to 1.40]; p < 0.001). No difference in recurrence rates was found between patients with osteosarcoma with pathologic fractures and those without (RR 1.22 [95% CI 0.91 to 1.64]; p = 0.18). However, having a pathologic fracture was associated with an increased risk of developing metastasis (RR 1.33 [95% CI 1.08 to 1.63]; p = 0.01). Treatment with limb salvage surgery was not associated with a higher rate of local recurrence (RR 1.58 [95% CI 0.88 to 2.85]; p = 0.13). CONCLUSION: In light of these findings, surgeons should be aware that after appropriate case selection, patients with osteosarcoma and pathologic fractures undergoing limb salvage surgery may have similar rates of local recurrence to those undergoing amputation. Therefore, a pathologic fracture may no longer be an absolute contraindication for limb salvage surgery. Future studies adjusting for potential confounders such as tumor size, tumor location, and response to neoadjuvant therapy would provide further insight into the effect of pathologic fractures on our assessed outcomes. LEVEL OF EVIDENCE: Level III, therapeutic study.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".