Use of the Rema Score to Distinguish Individuals with Systemic Mastocytosis From Those with Hereditary Alpha-tryptasemia
Bibliographic record
Abstract
Background: Systemic mastocytosis (SM) and hereditary alpha-tryptasemia (HαT) may present with overlapping clinical manifestations of mast cell activation, making them difficult to distinguish on clinical grounds. Diagnosing SM requires a bone marrow or tissue biopsy whereas HαT can be diagnosed with a buccal swab for genetic testing. Another potential method to differentiate SM from HαT is through a validated scoring system. For example, the Spanish Network on Mastocytosis, Red Española de Mastocitosis (REMA) score has been validated as a predictor of mast cell clonality in SM by using basal serum tryptase levels, clinical symptoms, and sex. This study aims to determine whether REMA scores can differentiate sufficiently between individuals with SM and HαT, thereby confidently ruling in or out the need for more invasive investigations such as bone marrow or tissue biopsy. Methods: A retrospective chart review was conducted on 39 patients with SM and 24 patients with HαT to calculate their individual REMA scores. A two-sample Wilcoxon test was conducted to assess the difference in median REMA scores between patients with SM and those with HαT. Within the SM cohort, subgroup analysis was performed to compare REMA scores based on the KIT D816V mutation and SM subtype. The area under the curve was calculated to evaluate the discriminatory property of the REMA score. Results: The Median REMA score within the SM cohort was 2 (0.50, 4.00) compared to -1 (-1.50 0.00) within the HαT cohort (p <0.001). REMA scores in patients with SM did not differ based on the KIT mutation status. A REMA score cut-off of 0.5 was able to distinguish SM and HαT with a specificity of 83.3% (67%,96%). Conclusion: This novel comparison of REMA scores in patients with SM and HαT highlights a potential role for the calculated REMA score in informing decisions about the need for invasive testing for patients presenting with symptoms of mast cell activation. However, larger comparative studies are needed before incorporating REMA scoring into routine care.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".