Predictors of delayed referral to a pediatric rheumatology center
Bibliographic record
Abstract
OBJECTIVE: To report on referral patterns of primary physicians for children subsequently diagnosed with juvenile idiopathic arthritis (JIA) and to identify predictors of delayed referral to a pediatric rheumatology center. METHODS: A retrospective cohort study of consecutive patients with JIA referred to a pediatric rheumatology center over a 15-year period was performed. Variables included age, sex, JIA subtype, the physician's subspecialty, and distance to the pediatric rheumatology center. Outcome parameters were the time to first presentation to a primary physician, the time to the first rheumatology visit, and the total time to referral. Putative predictors were evaluated by analysis of variance, resulting in regression models. RESULTS: A total of 132 patients with JIA were included; 83 (63%) were female. The median age at the onset of symptoms was 4.5 years (range 1.0-15.8 years). Most frequently, children were referred by pediatricians (49.4%) or orthopedic surgeons (34.1%). The median time to first presentation was short at 10 days (range 0-1,610 days). In contrast, the median time to first rheumatology visit was 60 days (range 0.0-2,100.0 days), resulting in a long median total time to referral of 90 days (range 0.0-2,160.0 days). Statistically significant predictors for delayed referral were the primary physician's subspecialty (P = 0.016) and the distance to the pediatric rheumatology center (P = 0.001). Children living in remote areas or referred by orthopedic surgeons had the longest referral times. CONCLUSION: Despite free access to health care in Germany, children with JIA are referred to pediatric rheumatology centers with significant delay. Educational interventions targeting primary physicians and orthopedic surgeons may contribute to earlier referral to pediatric rheumatology centers and improve outcome in patients with JIA.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".