The Relationship Between Tumor Budding and Clinical Pathological Characteristics of Cervical Cancer and Its Prognostic Significance:A Meta Analysis
Bibliographic record
Abstract
Objective: To explore the relationship between tumor budding and clinical pathological characteristics and prognosis of cervical cancer; Methods: By searching Pubmed, Embase, Cochren, CNKI and CBM databases, the research studies on the relationship between tumor budding and prognosis or clinic-pathological characteristics of cervical cancer were included. The quality of researches included was evaluated according the Castle Ottawa Scale (NOS) and statistically analyzed by Stata 12.0. Result: A total of 10 studies were included, including 11 cohorts. Including 2017 patients, the NOS score of these studies is more than 6 points. Tumor budding is related to age and tumor size of cervical cancer. In part of pathological characteristics, squamous cell carcinoma, higher stage, lymph node metastasis, distant metastasis, high WHO grade, deep interstitial invasion, Lymphatic vessel vascular space invasion, nerve invasion and para uterine invasion are more likely to occur tumor budding, occurrence of tumor sprouting in C-type cervical adenocarcinoma is higher than that in A/B type; In univariate analysis, there was a significant correlation between cervical cancer tumor sprouting and disease-free survival in patients (RR=6.511,P=0.000), in multivariate analysis, there was also a significant correlation between those (HR=3.358,P=0.001); Subgroup analysis of univariate analysis showed that Asian (RR=1.879, P=0.000), sample size ≤ 300 (RR=1.875, P=0.000), inclusion of squamous cell carcinoma (RR=1.884, P=0.000), staging criteria (FIGO and TNM), and tumor staging (I-II and mixed staging) all had an impact on DFS.Conclusion: The clinical application of tumor budding as a prognostic factor of cervical cancer can be considered.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.005 | 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 | 0.002 |
| 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".