American College of Rheumatology Criteria at Inception, and Accrual over 5 Years in the SLICC Inception Cohort
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency of each American College of Rheumatology (ACR) criterion met at time of enrollment, and the increase in each of the criteria over 5 years. METHODS: In 2000 the Systemic Lupus International Collaborating Clinics (SLICC) recruited an international inception cohort of patients with systemic lupus erythematosus (SLE; ≥ 4 ACR criteria) who were followed at yearly intervals according to a standard protocol. Descriptive statistics were used to assess the total and cumulative number of ACR criteria met at each visit. Regression models were done to compare the increase of individual and cumulative criteria as a function of race/ethnicity group, and sex. RESULTS: In all, 768 patients have been followed for a minimum of 5 years. Overall, 59.1% of the patients had an increase in the number of ACR criteria they met over the 5-year period. The mean number of ACR criteria met at enrollment was 5.04 ± 1.13 and at year 5 was 6.03 ± 1.42. At enrollment, nonwhite patients had a higher number of ACR criteria (5.19 ± 1.23) than white patients. The total number of criteria increased in both white and nonwhite ethnicities, but increased more among whites. Males had a slightly lower number of criteria at enrollment compared to females and males accrued fewer criteria at 5 years. CONCLUSION: In this international inception cohort of SLE patients with at least 4 ACR criteria at entry, there was an accumulation of ACR criteria over the following 5 years. The distribution of criteria both at inception and over 5 years is affected by sex and ethnicity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".