Extramedullary haematopoiesis in patients with thalassemia: a cross-sectional description of its prevalence, clinical features and survival
Bibliographic record
Abstract
INTRODUCTION: Despite knowledge advances on extramedullary haematopoiesis (EMH) in thalassemic patients, the real picture remains an open issue. OBJECTIVES: To assess EMH prevalence in patients with thalassemia major (TM) and intermedia (TI), to describe magnetic resonance imaging (MRI) findings and to explore clinical risk factors. METHODS: In this cross-sectional study, images and clinical records of 184 consecutive patients with thalassemia who underwent T2* MRI between 2004 and 2011 were reviewed. Association of EMH with survival was investigated for patients with available follow-up charts. RESULTS: EMH was detected in 16/168 (9.5%) patients with TM (aged 19-49 years) and in 3/16 (18.8%) with TI (aged 36-41 years). Most (88%) had paravertebral thoracic and/or abdominal masses. Age was significantly associated with EMH risk (hazard ratio, [HR] 1.10/year; confidence interval [CI]: 1.03-1.18; p-value < 0.001), while lower pancreatic iron content by T2*MRI (HR: 0.94/ms; CI: 0.89-0.99; p-value = 0.049) was a protective factor. Estimated survival rate was superior for EMH-positive (n = 19) when compared to EMH-negative patients (n = 75) (p-value = 0.013). CONCLUSIONS: The prevalence of EMH was 10.3% (19/184), presented mainly as tumoral masses of 3 to 10 cm. Age was a risk factor for EMH development, while lower pancreatic iron might be a protective factor in this cohort.
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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 | 0.001 |
| 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 | 0.001 |
| 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".