Effects of resection margins on local recurrence of osteosarcoma in extremity and pelvis: Systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: There are conflicting findings about the effect of resection margins on local recurrence in osteosarcoma after surgery. In this meta-analysis, we examined the association between local recurrence and resection margins for osteosarcoma in extremity and pelvis. METHODS: EMBASE, PubMed and Cochrane CENTRAL were searched from January 1980 to July 2016. The quality of included studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale. The odds ratio and 95% confidence interval of local recurrence were estimated, respectively, for inadequate vs adequate margins and marginal vs wide margins using a random-effect model. Chi-square test was performed to comparing the local recurrence rate between extremity and pelvic osteosarcomas with an identical surgical margin. RESULTS: Thirteen articles involving 1559 patients (175 with and 1384 without local recurrence) were included in this study. The meta-analysis showed that the osteosarcoma resected with inadequate and marginal margins, whether in extremity or in pelvis, were associated with a significantly higher local recurrence rate than the osteosarcoma resected with adequate and wide margins, respectively. Chi-square test showed that, when pelvic and extremity osteosarcomas were removed with an identical resection margin, the local recurrence was significantly more frequent in pelvis osteosarcoma than in extremity osteosarcoma. CONCLUSION: This study provides level IIa evidence to support that the surgery with adequate or wide resection margin has positive effect on reducing the risk of local recurrence in osteosarcoma. In addition, the factors independent of resection margin are more likely to increase the risk of local recurrence in pelvic osteosarcoma. LEVEL OF EVIDENCE: Level IIa, 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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".