Relative frequency of non‐Hodgkin lymphoma subtypes in selected centres in North Africa, the middle east and India: a review of 971 cases
Bibliographic record
Abstract
Comparative data regarding the distribution of non-Hodgkin lymphoma (NHL) subtypes in North Africa, the Middle East and India (NAF/ME/IN) is scarce in the literature. In this study, we evaluated the relative frequencies of NHL subtypes in this region. Five expert haematopathologists classified 971 consecutive cases of newly-diagnosed NHL from five countries in NAF/ME/IN. After review, 890 cases (91·7%) were confirmed to be NHL and compared to 399 cases from North America (NA). The male-to-female ratio was significantly higher in NAF/ME/IN (1·8) compared to NA (1·1; P< 0·05). The median ages of patients with low-grade (LG) and high-grade (HG) B-NHL in NAF/ME/IN (56 and 52 years, respectively) were significantly lower than in NA (64 and 68 years, respectively). In NAF/ME/IN, a significantly lower proportion of LG B-NHL (28·4%) and a higher proportion of HG B-NHL (58·4%) were found compared to NA (56·1% and 34·3%, respectively). Diffuse large B-cell lymphoma was more common in NAF/ME/IN (49·4%) compared to NA (29·3%), whereas follicular lymphoma was less common in NAF/ME/IN (12·4%) than in NA (33·6%). In conclusion, we found significant differences in NHL subtypes and clinical features between NAF/ME/IN and NA. Epidemiological studies are needed to better understand the pathobiology of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| 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".