Population-based prevalence and incidence estimates of mixed connective tissue disease from the Manhattan Lupus Surveillance Program
Bibliographic record
Abstract
OBJECTIVE: Epidemiological data for MCTD are limited. Leveraging data from the Manhattan Lupus Surveillance Program (MLSP), a racially/ethnically diverse population-based registry of cases with SLE and related diseases including MCTD, we provide estimates of the prevalence and incidence of MCTD. METHODS: MLSP cases were identified from rheumatologists, hospitals and population databases using a variety of International Classification of Diseases, Ninth Revision codes. MCTD was defined as one of the following: fulfilment of our modified Alarcon-Segovia and Kahn criteria, which required a positive RNP antibody and the presence of synovitis, myositis and RP; a diagnosis of MCTD and no other diagnosis of another CTD; and a diagnosis of MCTD regardless of another CTD diagnosis. RESULTS: Overall, 258 (7.7%) cases met a definition of MCTD. Using our modified Alarcon-Segovia and Kahn criteria for MCTD, the age-adjusted prevalence was 1.28 (95% CI 0.72, 2.09) per 100 000. Using our definition of a diagnosis of MCTD and no other diagnosis of another CTD yielded an age-adjusted prevalence and incidence of MCTD of 2.98 (95% CI 2.10, 4.11) per 100 000 and 0.39 (95% CI 0.22, 0.64) per 100 000, respectively. The age-adjusted prevalence and incidence were highest using a diagnosis of MCTD regardless of other CTD diagnoses and were 16.22 (95% CI 14.00, 18.43) per 100 000 and 1.90 (95% CI 1.49, 2.39) per 100 000, respectively. CONCLUSIONS: The MLSP provided estimates for the prevalence and incidence of MCTD in a diverse population. The variation in estimates using different case definitions is reflective of the challenge of defining MCTD in epidemiologic studies.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".