Meta-analysis of Relationship Between Expression Level of Human Epididymis Protein 4 and Lymph Node Metastasis in Endometrial Cancer
Bibliographic record
Abstract
Objective To evaluate systematically the correlation between the expression level of human epididymis protein 4 (HE4) and lymph node metastasis of endometrial cancer (EC). Methods Computers were used to search for the literatures about the correlation between the expression level of HE4 and lymph node metastasis of EC in PubMed, Cochrane, Web of Science, CBM, CNKI, and Wanfang Database. The search time was from the database establishment to May 2021. Articles were screened in accordance with the inclusion and exclusion criteria, and the quality of literature was evaluated by Newcastle Ottawa scale. Stata12.0 was used to perform meta-analysis, and TSA was used to evaluate the sample size. Results A total of 2736 patients with EC were included in the 25 eligible studies. The results of meta-analysis showed that the expression level of HE4 in the EC-lymph-node metastasis group was significantly higher than that in the non-metastasis group (SMD=1.58, 95%CI: 1.13-2.03), and meta-regression analysis revealed that the results were related to the average age of patients in each study. TSA analysis exhibited that the total sample size of the included studies met the requirements. Conclusion The expression level of HE4 is correlated with lymph node metastasis of EC, and this correlation may be affected by age, body mass index, and other factors. The correlation weakens with the increase in age.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".