Prognostic value of pathologic fracture in patients with high grade localized osteosarcoma: A systemic review and meta‐analysis of cohort studies
Bibliographic record
Abstract
Consensus has not been reached regarding the ability of pathologic fracture to predict local recurrence and survival in osteosarcoma. We aim to review the available evidence to examine the association between pathologic fracture and osteosarcoma prognosis. A comprehensive literature search for relevant studies published until March 2014 was performed using PubMed, Cochrane and Web of Science. The studies investigating pathologic fracture of osteosarcoma patients were systematically analyzed. The overall relative risk (RR) was estimated using a fixed-effect model or random-effect model according to heterogeneity between the trials. We included nine cohort studies involving 2,187 patients (311 with pathologic fracture and 1,876 without fracture) for the analysis of survival rate and local recurrence. Studies were assessed for quality using the Newcastle-Ottawa Assessment Scale. In the fixed-effects model, the meta-analysis showed that pathologic fracture in osteosarcoma patients predicted poor 3-year overall survival (OS) (RR=1.86, 95% CI: 1.37-2.53, p<0.001) and 5-year OS (RR=1.34, 95% CI: 1.06-1.70, p=0.016). Similarly, pathologic fracture was significantly correlated with worse 3-year event free survival (EFS) (RR=1.52, 95% CI: 1.21-1.92, p<0.001) and 5-year EFS (RR=1.24, 95% CI: 1.03-1.49, p=0.021), whereas no significant association was noted with local recurrence (RR=1.30, 95% CI: 0.84-2.02, p=0.233). The meta-analysis confirmed that pathologic fracture in osteosarcoma was a prognostic marker for both OS and EFS but not for local recurrence.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".