High-grade tumor budding influences prognosis of I-II stage cervical cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: The aim of this study is to investigate the prognostic correlation between tumor budding and stage I-II cervical cancer, with the goal of providing guidance for postoperative diagnosis and treatment strategies for patients.Methods: A comprehensive search was conducted across 12 databases including Pubmed, Cochrane, Embase, Scopus, OVID, Web of Science, EBSCohost, CNKI, Wan-Fang, VIP, Dui-Xiu and CBM to identify relevant literature on the association between tumor budding and prognosis or clinicopathological features of cervical cancer.The quality of included studies was assessed using the Newcastle-Ottawa scale. Statistical analysis was performed using Review Manager.Results: Our findings demonstrate that high-grade tumor budding in stage I-II cervical cancer is associated with significantly poorer overall survival (P<0.0001) and disease-free survival (P<0.0001). Subgroup analyses revealed that irrespective of sample size and histological type, the overall survival in the high-grade tumor budding group is markedly lower than that in the low-grade tumor budding group; similarly regardless of stage inclusion criteria, budding type, field boundary value or sample size,the disease-free survival in the high-grade tumor budding group is significantly lower than that in the low-grade tumor budding group.Furthermore,high grade tumor budding is correlated with several adverse pathological features.Conclusion: In light of these results,it can be concluded that tumor budding serves as an unfavorable prognostic factor for stage I-II cervical cancer,and may inform I-II stage postoperative treatment planning for such patients.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".