Bibliographic record
Abstract
目的：探究MMP9蛋白在良性甲状腺结节与甲状腺恶性肿瘤的表达差异。方法：通过检索2020年12月31日之前PubMed、Web of Science、CNKI和万方数据库关于MMP9蛋白水平与甲状腺恶性肿瘤关系的病例对照研究。根据相应标准选择文献并提取相关的数据，采用纽卡斯尔–渥太华量表评价其方法学质量，提取的资料通过Stata12.0软件进行统计学分析，I2检验评价文章的异质性。结果：共纳入10篇符合要求的文献，其中包括554名诊断为甲状腺恶性肿瘤患者和337名良性甲状腺结节患者。甲状腺恶性肿瘤患者组织中MMP9蛋白水平明显高于良性甲状腺结节组，差异有统计学意义(OR = 9.80, 95% CI: 5.77~16.62, P Objective: To investigate the difference of MMP9 protein expression in benign thyroid nodules and thyroid cancer. Methods: Case-control studies on the relationship between MMP9 protein levels and thyroid cancer by searching PubMed, Web of Science, CNKI and Wanfang databases before December 31, 2020. The literature was selected and relevant data were extracted according to the inclusion and exclusion criteria, and the methodological quality was evaluated using the Newcastle-Ottawa scale, and the extracted data were statistically analyzed by Stata 12.0 software, and the I2 test was used to evaluate the heterogeneity of the articles. Results: A total of 10 eligible articles, including 554 patients diagnosed with thyroid cancer and 337 patients with benign thyroid nodules were included. MMP9 protein levels were significantly higher in the samples from patients with thyroid cancer than in the group with benign thyroid nodules, with a statistically significant difference (OR = 9.80, 95% CI: 5.77~16.62, P < 0.05). Meta-analysis by dividing thyroid cancer into multiple subgroups by different clinical features showed that high expression of MMP9 was associated with larger tumor diameter (OR = 3.37, 95% CI: 1.36~8.34, P < 0.05), the presence of envelope invasion (OR = 13.95, 95% CI: 4.13~47.09), lymph node metastasis (OR = 6.05, 95% CI: 3.28~11.15, P < 0.05) and higher TNM stage (OR = 5.03, 95% CI: 2.40~10.56, P < 0.05) were closely associated. Conclusion: MMP9 protein is highly expressed in thyroid cancer tissues, closely correlated with tumor diameter size, envelope invasion, lymph node metastasis, and clinical stage, and is expected to be a new therapeutic target for thyroid cancer.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.003 | 0.005 |
| 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".