The role of adiponectin (<i>ADIPOQ</i>) gene polymorphisms in the susceptibility and prognosis of non-small cell lung cancer
Bibliographic record
Abstract
To study the role of the adiponectin (ADIPOQ) gene single-nucleotide polymorphism (SNP) in the susceptibility and prognosis for non-small cell lung cancer (NSCLC), we recruited 344 patients with NSCLC, of which 141 had undergone surgical resection and post-surgery follow up. For controls, there were 264 healthy volunteers for the control group, matched in age and sex with the NSCLC patients. Genotyping of SNPs in the ADIPOQ gene, namely, rs266729 (11365C>G); rs822395 (4034A>C); rs822396 (3964A>G); rs2241766 (+45T>G) were performed. Of all SNPs in the ADIPOQ gene, only the TT genotype and T allele frequency of the rs2241766 were more prevalent in NSCLC subjects than in controls. The TT genotype of rs2241766 was significantly associated with susceptibility to NSCLC before and after adjustment for age, sex, body mass index, and smoking status. In the survival analyses of subjects receiving surgical resection, only the SNPs of rs2241766 were significantly related to overall survival of NSCLC. Our results suggest that the SNP rs2241766 of the ADIPOQ gene may determine both susceptibility to NSCLC, and the prognosis for those who underwent surgical treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".