Association between cadherin-17 expression and pathological characteristics of gastric cancer: A meta-analysis
Bibliographic record
Abstract
AIM: To construct a meta-analysis in order to examine the relationship between cadherin-17 (CDH17) and gastric cancer (GC). METHODS: Related articles were selected by searching the following English or Chinese electronic databases: CINAHL, MEDLINE, Science Citation Index, the Chinese Journal Full-Text, and the Weipu Journal. Newcastle-Ottawa Scale (NOS) criteria were used to ensure consistency in reviewing and reporting results. Statistical analyses were conducted with Version 12.0 STATA statistical software. RESULTS: Ultimately, 11 articles, with a total of 2,120 GC patients, were found to be eligible for study inclusion. In comparisons of GC patients by TNM stage (III-IV vs I-II: OR = 2.35, 95%CI: 1.15-4.825, P = 0.019), histologic grade (3-4 vs 1-2: OR = 3.48, 95%CI: 1.36-8.92, P = 0.009), invasion grade (T3-4 vs T1-2: OR = 2.86; 95%CI: 1.69-4.83; P = 0.000), and lymph node metastasis (positive vs negative: OR = 2.64; 95%CI: 1.33-5.27; P = 0.006), it was found that CDH17 showed more positive expressions in each of the more severe cases. Country-stratified analyses from all four experimental subgroups showed that high CDH17 expression levels may be related to GC among Chinese and Korean populations (all P < 0.05), with the exception of the invasion grade T3-4 vs T1-2 comparison, where the relation only held among the Chinese population (OR = 2.86, 95%CI: 1.69-4.83, P = 0.000). CONCLUSION: Collectively, the data reflects the capacity of CDH17 in tumor proliferation and metastasis among GC 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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".