Limb-salvage surgery offers better five-year survival rate than amputation in patients with limb osteosarcoma treated with neoadjuvant chemotherapy. A systematic review and meta-analysis
Bibliographic record
Abstract
Osteosarcoma is the most common primary bone sarcoma. Currently, the main treatment option for high-grade osteosarcomas is neoadjuvant chemotherapy, followed by surgical resection of the lesion and adjuvant chemotherapy. Limb salvage surgery (LSS) and amputation are the main surgical techniques; however, controversy still exists concerning the best surgical method. Our meta-analysis compared the effectiveness of LSS and amputation combined with neoadjuvant chemotherapy in patients with limb osteosarcoma, in terms of 5-year overall survival (OS), 5-year disease-free survival (DFS) and local recurrence rate. Following the established methodology of PRISMA guidelines, a literature search was conducted in PubMed, Cochrane, Google Scholar from 1975 until January 2020. Two independent reviewers evaluated the study quality based on the Newcastle-Ottawa scale. Odds ratio and 95% confidence interval of the OS, DFS and local recurrence rate were calculated. Thirteen studies were finally included with a total of 2884 patients; 1986 patients undergone LSS and 898 amputations. Five-year overall survival was almost 2-fold in patients treated with LSS than those treated with amputation (OR: 1.99; 95% CI: 1.35–2.93; I2 = 74%, p < 0.001). No difference was found in 5-year DFS between LSS patients and amputees (OR: 1.24; 95% CI: 0.55–2.79; I2 = 67%, p = 0.01). The odds of local recurrence was numerically higher in LSS compared to amputation but not statistically significant (OR: 2.29; 95% CI: 0.95–5.53; I2 = 47%, p = 0.05). However, the included studies did not clearly define differences in the stages of patients of the two groups. Our study demonstrated that in patients with limb osteosarcoma treated with neoadjuvant chemotherapy, LSS is associated with a higher 5-year overall survival and the odds of local recurrence may be increased but these results should be interpreted with caution due to high heterogeneity.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.041 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".