Bibliographic record
Abstract
Women with rheumatic diseases, including inflammatory arthritis and systemic lupus erythematosus (SLE), fare better in pregnancy when their disease is under good control1,2. The role of hydroxychloroquine (HCQ) for achieving this control is now recognized. Several studies demonstrate that patients with SLE who continue HCQ during pregnancy have decreased flares and improved pregnancy outcomes, including longer fetal gestation and infants with higher birth weight3,4. Reassuringly, antimalarials are compatible with pregnancy, with no signals for safety concerns, and professional society guidelines recommend continuation of HCQ during pregnancy5. In support of this approach, a survey of North American rheumatologists found that over 69% of rheumatologists continued HCQ in their pregnant patients6. Despite the recognition that HCQ is a key component of SLE disease management in both nonpregnant and pregnant patients, adherence to HCQ remains abysmally low. Feldman, et al used claims data to show that 79% of nonpregnant patients with SLE are nonadherent7. Although claims data have also showed that overall use of HCQ during pregnancy has improved from 12.4% in 2004 to 37.7% in 2015, these rates are far from optimal8. Thus, low adherence to HCQ confounds conclusions regarding this drug’s effect on disease control and pregnancy outcome in SLE. To circumvent the possible effect of medication nonadherence on the understanding of the role of HCQ in rheumatic disease management, literature has focused on the measurement of either whole blood or serum drug levels and correlating these levels to disease activity. Costedoat-Chalumeau, et al were among the … Address correspondence to Dr. B.L. Bermas, UT Southwestern Medical Center, Rheumatic Diseases, 2001 Inwood Road, Dallas, Texas 75390, USA. E-mail: Bonnie.Bermas{at}UTSouthwestern.edu
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".