Consensus recommendations for the diagnosis and clinical management of Rosai-Dorfman-Destombes disease
Bibliographic record
Abstract
Abstract Rosai-Dorfman-Destombes disease (RDD) is a rare non–Langerhans cell histiocytosis characterized by accumulation of activated histiocytes within affected tissues. RDD, which now belongs to the R group of the 2016 revised histiocytosis classification, is a widely heterogeneous entity with a range of clinical phenotypes occurring in isolation or in association with autoimmune or malignant diseases. Recent studies have found NRAS, KRAS, MAP2K1, and ARAF mutations in lesional tissues, raising the possibility of a clonal origin in some forms of RDD. More than 1000 reports have been published in the English literature; however, there is a lack of consensus regarding approach for the clinical management of RDD. Although in most cases RDD can be observed or treated with local therapies, some patients with refractory or multifocal disease experience morbidity and mortality. Here we provide the first consensus multidisciplinary recommendations for the diagnosis and management of RDD. These recommendations were discussed at the 32nd Histiocyte Society Meeting by an international group of academic clinicians and pathologists with expertise in RDD. We include guidelines for clinical, laboratory, pathologic, and radiographic evaluation of patients with RDD together with treatment recommendations based on clinical experience and review of the literature.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".