Associations Between Tumor Necrosis Factor-α (TNF-α) −308 and −238 G/A Polymorphisms and Shared Epitope Status and Responsiveness to TNF-α Blockers in Rheumatoid Arthritis: A Metaanalysis Update
Bibliographic record
Abstract
OBJECTIVE: To investigate whether tumor necrosis factor-alpha (TNF-alpha) promoter -308 A/G and -238 A/G polymorphisms and shared epitope (SE) status are associated with responsiveness to anti-TNF therapy in patients with rheumatoid arthritis (RA). METHODS: A comparative metaanalysis was conducted on A allele carriers (genotypes A/A + A/G) of the TNF-alpha promoter -308 and -238 A/G polymorphisms and SE status in responders and nonresponders to anti-TNF therapy. RESULTS: A total of 13 studies were included in the metaanalysis. Metaanalysis showed that the TNF-alpha -308 A/G polymorphism is not associated with responsiveness to TNF blockers in RA patients. Studies with a small number of subjects (< 100) showed that the odds ratio for the A allele carrier state was significantly lower among responders (OR 0.344, 95% CI 0.152-0.779, p = 0.01). Studies with a higher number of subjects (>or= 100) found no association between the TNF-alpha -308 A/G polymorphism and responsiveness to TNF blockers. The overall metaanalysis showed that the TNF-alpha -238 A/G polymorphism was not associated with the responsiveness of RA patients to TNF blockers, and stratification by TNF blocker revealed that the TNF-alpha -238 A/G polymorphism was associated with response of infliximab (OR 0.441, 95% CI 0.203-0.609, p = 0.039). SE status was found not to be associated with response to TNF blockers. CONCLUSION: Metaanalysis of available data revealed an association between treatment response to infliximab and the TNF-alpha -238 A/G polymorphism, but no associations between treatment response and the TNF-alpha -308 A/G polymorphism or SE status.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.037 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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".