Protocol for the systematic review and network meta-analysis of open versus video-assisted and robotic-assisted thymectomy for the treatment of thymic neoplasms
Bibliographic record
Abstract
Abstract The surgical management of thymic neoplasms includes open and minimally invasive approaches. Previous studies have compared these techniques, but application in practice remains varied. This systematic review and network meta-analysis (NMA) will adhere to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) checklist. MEDLINE, Embase, Cochrane Centrale and Scopus will be searched from inception to perform a systematic review, NMA and evidence appraisal using the Grading of Recommendations Assessment, Development and Evaluations and the Confidence in Network Meta-Analysis methodologies. Randomized controlled trials and cohort studies will be included. Full texts of any citation will be included if they assessed a minimum of two arms of any type of thymectomy technique, including open, video-assisted thoracoscopic surgery, or robotic-assisted thoracoscopic surgery thymectomy, for the treatment of thymic neoplasms such as thymoma, thymic carcinoma or thymic neuroendocrine tumors with or without myasthenia gravis. Studies assessing operative thymectomy techniques for benign disease will be excluded. Short- and long-term perioperative safety and oncologic outcomes will be compared between open versus video-assisted versus robotic-assisted thymectomy for the surgical management of thymic neoplasms. The Risk of Bias In Non-Randomized Studies—of Interventions tool will be used to assess the risk of bias in nonrandomized studies. We will conduct a frequentist fixed- and random-effects NMA using the graph theory approach for each outcome. Summary of odds ratios will be estimated for all dichotomous outcomes with their 95% confidence interval.
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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 | 0.088 | 0.164 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.162 | 0.018 |
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".