Assessment of an infectious disease history preceding juvenile dermatomyositis symptom onset
Bibliographic record
Abstract
OBJECTIVES: A number of studies have looked at the role of infectious diseases in triggering juvenile dermatomyositis (JDM). Previous studies have found a moderately high frequency of infectious symptoms prior to disease onset; however, no specific pathogens could be identified. We sought to correlate preceding infectious symptoms with onset and outcomes of JDM. METHODS: We studied an inception cohort of all JDM cases diagnosed at The Hospital for Sick Children (SickKids) between 1988 and 2006. Data pertaining to symptoms at onset, diagnosis and disease outcomes were abstracted. Two independent paediatric infectious disease specialists reviewed all records of patients with symptoms or tests suggestive of infection. RESULTS: A total of 110 patients were reviewed; of these, 78 had sufficient information about disease onset for inclusion. Potential indications of an infectious process prior to JDM onset were found in 55/78 (71%) patients and were further evaluated for evidence of infection temporally associated with symptom onset. Features suggestive of infection prior to JDM symptom onset were found in 40/55 [probable (30/40) or possible (10/40)]. Most children with probable infections had respiratory illnesses [24/30 (80%)]. Fewer patients than expected had disease onset during summer months. The presence of an infection at onset was not found to be associated with differences in characteristics at diagnosis or disease outcomes. CONCLUSIONS: A substantial number of JDM patients have a clinical history consistent with an infection prior to onset. Newly diagnosed patients should undergo a full infectious disease assessment as part of their initial work-up; specific attention should be given to respiratory infections.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".