Prevalence, management and burden of mastocytosis from the physician’s perspective: A nationwide study
Bibliographic record
Abstract
Background: Mastocytosis is characterized by the accumulation of abnormal mast cells in various organs. Data on the prevalence of mastocytosis are heterogeneous, with the condition’s prevalence estimated to be between 9.6 and 23.9 per 100,000 inhabitants. Patients may present signs and symptoms that can severely impact quality of life (QoL), but reported data are scarce. Thus, we performed a nationwide study to estimate the prevalence and to assess the management and burden of adults with mastocytosis in France according to physician assessments. Methods: We developed an online survey comprising 25 questions investigating various aspects of mastocytosis and asked 6,239 physicians to respond. Data concerning physician characteristics and the number of patients followed were used to estimate overall prevalence. To assess patients’ QoL, we focused on the presence of signs and symptoms and the patients’ burden specifically in those with either indolent systemic mastocytosis (ISM) or mastocytosis in the skin (MIS). Results: Between July 11, 2023, and September 1, 2023, 1,169 physicians (18.7%) completed the survey. These physicians managed 4,121 mastocytosis patients, corresponding to an estimated prevalence of mastocytosis of 8.5 per 100,000 in France. In the ISM/MIS population (representing 76% of mastocytosis patients), 53% presented moderate to severe symptoms (mainly skin, digestive and general symptoms). Overall, physicians indicated that there was substantial burden associated with these symptoms in almost all fields of QoL analyzed. Conclusions: Our results provide further evidence of the burden associated with mastocytosis and highlight the need to improve QoL in these patients.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".