Bibliographic record
Abstract
Background: Tumour necrosis factor a (TNFa) is a cytokine of critical importance in psoriatic arthritis. Objectives: (1) To examine the association between TNFa promoter gene polymorphisms and psoriatic arthritis in two well characterised Canadian populations with the disease; (2) to carry out a meta-analysis of all TNFa association studies in white psoriatic arthritis populations. Methods: DNA samples were genotyped for five TNF variants by time of flight mass spectrometry using the Sequenom platform. All five single nucleotide polymorphisms were in the 59 flanking region of TNFa gene at the following positions:21031 (TRC),2863 (CRA),2857 (CRT),2308 (GRA), and2238 (GRA). Primary analyses were based on logistic regression. Summary estimates of disease/genotype relations from several studies were derived from random effects meta-analyses. Results: 237 psoriatic arthritis subjects and 103 controls from Newfoundland and 203 psoriatic arthritis subjects and 101 controls from Toronto were studied. A combined analysis of data from both populations, showed a significant association between disease status and the 2238(A) variant (p = 0.01). The meta-analysis estimate for the 2238(A) TNFa variant in eight psoriatic arthritis populations was also significant (odds ratio = 2.29 (95 % confidence interval, 1.48 to 3.55)). Conclusions: Analysis of TNFa variants in psoriatic arthritis populations shows that the 2238 (A) variant is
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.878 | 0.784 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".