Comparison of Threshold Cutpoints and Continuous Measures of Anti-Cyclic Citrullinated Peptide Antibodies in Predicting Future Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Anti-cyclic citrullinated peptide (anti-CCP) antibodies are strongly associated with increased risk of rheumatoid arthritis (RA).While the anti-CCP level is commonly dichotomized for clinical use, the best threshold for and utility of the titer as a continuous variable to predict development of RA are uncertain. METHODS: Using data from the Nurses' Health Study and Nurses' Health Study II longitudinal cohorts, we examined the sensitivity, specificity, and hazard of RA at various thresholds of the anti-CCP. Incident RA was confirmed using the Connective Tissue Disease Screening Questionnaire and medical record review in 93 women from among 62,437 participants with blood samples. Three controls per case were randomly chosen, matching on cohort, age, and menopausal status. Stored plasma was tested for anti-CCP antibodies with the second-generation Diastat ELISA. Five threshold values were assessed for sensitivity, specificity, and time to diagnosis of RA. Hazard of RA was assessed with conditional logistic regression models adjusting for smoking and reproductive factors. RESULTS: Using the suggested threshold of >5 U/ml for anti-CCP positivity, specificity was 100%, but sensitivity was only 28%. A threshold of >2 U/ml had a higher sensitivity (51%), and similar specificity (80%), with an odds ratio of 11.2 (95% confidence interval 4.7-26.9) for RA. Anti-CCP level as an ordinal variable was strongly associated with time to RA onset, with higher values predicting shorter time to RA onset. CONCLUSION: A lower threshold for anti-CCP positivity was more sensitive in predicting RA development. Higher ranges of the level were informative in predicting time to RA onset.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".