Expert consensus on dynamics of laboratory tests for diagnosis of macrophage activation syndrome complicating systemic juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVE: To identify which laboratory tests that change over time are most valuable for the timely diagnosis of macrophage activation syndrome (MAS) complicating systemic juvenile idiopathic arthritis (sJIA). METHODS: A multistep process, based on a combination of expert consensus and analysis of real patient data, was conducted. A panel of experts was first asked to evaluate 115 profiles of patients with MAS, which included the values of laboratory tests at the pre-MAS visit and at MAS onset, and the change in values between the two time points. The experts were asked to choose the 5 laboratory tests in which change was most important for the diagnosis of MAS and to rank the 5 selected tests in order of importance. The relevance of change in laboratory parameters was further discussed and ranked by the same experts at a consensus conference. RESULTS: Platelet count was the most frequently selected test, followed by ferritin level, aspartate aminotransferase (AST), white cell count, neutrophil count, and fibrinogen and erythrocyte sedimentation rate. Ferritin was most frequently assigned the highest score. At the end of the process, platelet count, ferritin level and AST were the laboratory tests in which the experts found change over time to be most important. CONCLUSIONS: We identified the laboratory tests in which change over time is most valuable for the early diagnosis of MAS in sJIA. The dynamics of laboratory values during the course of MAS should be further scrutinised in a prospective study in order to establish the optimal cut-off values for their variation.
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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 | 0.095 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".