Diagnostic evaluation and medication usage in a cohort of subjects with juvenile dermatomyositis from the CARRAnet registry
Bibliographic record
Abstract
Children under 21 yrs with onset of JDM prior to 16 yrs were included, and subjects or their guardians were consented for the study. IRB approval was obtained at each enrolling site. JDM was diagnosed by modified Bohan and Peter criteria. Clinical data were collected from the subjects, guardians, and providers using both general and JDM-specific case report forms at the time of enrollment. Data regarding demographics, diagnostic assessment, and medication exposure were collected. Data were pooled and stored in a secure centralized database and de-identified prior to analysis. Between May 28, 2010 and December 28, 2010, 102 subjects meeting modified criteria for JDM were enrolled from 23 sites in the U.S. Diagnostic studies commonly used include electromyography (EMG), muscle biopsy, and magnetic resonance imaging (MRI). Overall, MRI was more likely than EMG or muscle biopsy to show abnormalities. (Table 1 ) 48 of subjects had 2 or more studies performed and 54.2% of these subjects reported at least 1 negative study. In terms of medications, 100% of subjects have been exposed to corticosteroids during their course of treatment, and 97% of subjects have been exposed to methotrexate, suggesting that these medications are almost universally prescribed for JDM. Medication history in order of frequency of usage is shown in Table 2 . MRI was the most common diagnostic modality used and was the most likely to show abnormalities consistent with JDM. The false negative rates for MRI, EMG, and muscle biopsy alone were higher than expected if ascertainment is correct. Corticosteroids and methotrexate appear to be standard first line medications used by US pediatric rheumatologists for JDM. Pulse corticosteroids, intravenous gammaglobulin, and hydroxychloroquine have been used by about half of subjects and further investigation as to which subgroups receive these medications is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".