Antinuclear antibody prevalence in a general pediatric cohort from Mexico City: discordance between immunofluorescence and multiplex assays
Bibliographic record
Abstract
OBJECTIVE: To characterize antinuclear antibody (ANA) prevalence according to distinct assay methodologies in a pediatric cohort from Mexico City, and to further examine associations with age and sex. METHODS: Serum ANA were measured by indirect immunofluorescence assay (IFA) and multiplex immunoassay in 114 children aged 9-17 years. IFA was considered positive at a cutoff titer of ≥1:80. Agreement between assay methods was assessed by kappa statistic. Sensitivity, specificity, and 95% confidence intervals (CIs) of the multiplex were computed with IFA as the reference standard. RESULTS: Of the 114 children (mean age 14.7 [standard deviation 2.1] years; 54 [47%] female), 18 of 114 (15.8%) were ANA positive by IFA, and 11 of 114 (9.6%) by 11-antigen multiplex assay. ANA prevalence was higher in females compared with males by both of the methods (ratios 1.6-1.9 to 1). Agreement between tests was classified as slight by kappa (κ=0.177 [95% CI -0.051, 0.406]). The multiplex immunoassay had sensitivity of 22.2% (95% CI 6.4, 47.6) and specificity of 92.7% (95% CI 85.6, 97.0), and failed to capture 3 of 4 (75%) of the high-titer (≥1:1280) IFA-positives. CONCLUSION: Up to 15% of children in this general population cohort were ANA positive, with a higher rate of positivity among females according to both assay methods. Substantial discordance in ANA results was found between IFA and multiplex methods, even for high-titer IFA positives. These findings underscore the need to sufficiently account for assay characteristics when interpreting ANA test results, and support IFA as the more appropriate assay for studies of subclinical autoimmunity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".