How I diagnose and treat systemic mastocytosis with an associated hematologic neoplasm
Bibliographic record
Abstract
ABSTRACT: Over the last decade significant advances have been made by honing the diagnostic evaluation and the significance of molecular profiles in patients with nonadvanced and advanced systemic mastocytosis (AdvSM). This is reflected in the 2022 iterations of the World Health Organization edition 5 and International Consensus Criteria classifications. The impact of targeted KIT inhibitor therapies on patients treated within global trials has demonstrated significant improvements in the prognosis and overall survival for patients, leading to a change in the treatment paradigm. Patients with SM and an associated hematologic neoplasm (AHN) comprise up to 70% of those in the advanced SM category, posing varying challenges in diagnosis and clinical heterogeneity because of the occupation of the bone marrow niche by 2 hematologic neoplasms. We are constantly learning about the complex, heterogenous genotypic and phenotypic spectrum of these patients with a view to provide personalized treatment options, aiming to improve outcomes, quality of life, and ultimately a cure. This paper focuses on the management of patients with AdvSM with an AHN and is a personal perspective using some illustrative patient cases treated at our center, Guy's and St Thomas' Hospitals, London, UK center of excellence in mastocytosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".