Prognosis and surgical outcomes of the total thymectomy versus thymomectomy in non-myasthenic patients with early-stage thymoma: A systematic review and meta-analysis
Bibliographic record
Abstract
Whether thymectomy (TM) or thymomectomy (TMM) is better for non-myasthenic patients with early-stage thymoma. We conducted a meta-analysis to compare the clinical outcomes and prognoses of non-myasthenic patients with early-stage thymoma treated using thymectomy versus thymomectomy. PubMed, Embase, Cochrane Library and CNKI databases were systematically searched for relevant studies on the surgical treatment (TM and TMM) of non-myasthenic patients with early-stage thymoma published before March 2022. The Newcastle-Ottawa scale was used to evaluate the quality of the studies, and the data were analyzed using RevMan version 5.30. Fixed or random effect models were used for the meta-analysis depending on heterogeneity. Subgroup analyses were performed to compare short-term perioperative and long-term tumor outcomes. A total of 15 eligible studies, including 3023 patients, were identified in the electronic databases. Our analysis indicated that TMM patients might benefit from a shorter duration of surgery (p = 0.006), lower blood loss volume (p < 0.001), less postoperative drainage (p = 0.03), and a shorter hospital stay (p = 0.009). There were no significant differences in the overall survival rate (p = 0.47) or disease-free survival rate (p = 0.66) between the two surgery treatment groups. Likewise, TM and TMM were similar in the administration of adjuvant therapy (p = 0.29), resection completeness (p = 0.38), and postoperative thymoma recurrence (p = 0.99). Our study revealed that TMM might be a more appropriate option in treating non-myasthenic patients with early-stage thymoma.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".