Classification of non‐Hodgkin lymphoma in South‐eastern Europe: review of 632 cases from the international non‐Hodgkin lymphoma classification project
Bibliographic record
Abstract
The distribution of non-Hodgkin lymphoma (NHL) subtypes varies around the world, but a systematic study of South-eastern Europe (SEEU) has never been done. Therefore, we evaluated the relative frequencies of NHL subtypes in three SEEU countries--Croatia, Romania and Macedonia. Five expert haematopathologists reviewed 632 consecutive cases of newly diagnosed NHL from the three SEEU countries using the World Health Organization classification. The results were compared to 399 cases from North America (NA) and 580 cases from Western Europe (WEU). The proportions of B- and T-cell NHL and the sex distribution in SEEU were similar to WEU and NA. However, the median ages of patients with low- and high-grade B-NHL in SEEU (60 and 59 years, respectively) were significantly lower than in NA (64 and 68 years, respectively; P < 0·05). SEEU had a significantly lower proportion of low-grade B-NHL (46·6%) and higher proportion of high-grade B-NHL (44·5%) compared to both WEU (54·5% and 36·4%, respectively) and NA (56·1% and 34·3%, respectively). There were no significant differences in the relative frequencies of T-NHL subtypes. This study provides new insights into differences in the relative frequencies of NHL subtypes in different geographic regions. Epidemiological studies are needed to better characterize and explain these differences.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".