Meta-Analysis of Limited Thymectomy versus Total Thymectomy for Masaoka Stage I and II Thymoma
Bibliographic record
Abstract
Background: This meta-analysis aimed to evaluate the incidence of tumor recurrence, postoperative myasthenia gravis, postoperative complications, and overall survival after limited versus total thymectomy for Masaoka stage I and II thymoma.Methods: A systematic search of the literature was conducted using the PubMed, Embase, MEDLINE, and Cochrane databases to identify relevant studies that compared limited and total thymectomy in Masaoka stage I-II patients.The quality of the included observational studies was assessed using the Newcastle-Ottawa Scale.The results of the meta-analysis were expressed as log-transformed odds ratios (log ORs), with 95% confidence intervals (CIs).Results: Seven observational studies with a total of 2,310 patients were included in the meta-analysis.There was an overall non-significant difference in favor of total thymectomy in terms of tumor recurrence (pooled log OR, 0.40; 95% CI, -0.07 to 0.87; p=0.10;I 2 =0%) and postoperative myasthenia gravis (pooled log OR, 0.12; 95% CI, -1.08 to 1.32; p=0.85;I 2 =22.6%).However, an overall non-significant difference was found in favor of limited thymectomy with respect to postoperative complications (pooled log OR, -0.21; 95% CI, -1.08 to 0.66; p=0.64;I 2 =36.1%) and overall survival (pooled log OR, -0.01; 95% CI, -0.68 to 0.66; p=0.98;I 2 =47.8%). Conclusion:Based on the results of this systematic review and meta-analysis, limited thymectomy as a treatment for stage I and II thymoma shows similar oncologic outcomes to total thymectomy.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.063 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".