Non‐Hodgkin lymphoma in Southern Africa: review of 487 cases from The International Non‐Hodgkin Lymphoma Classification Project
Bibliographic record
Abstract
Comparative data on the distribution of non-Hodgkin lymphoma (NHL) subtypes in Southern Africa (SAF) is scarce. In this study, five expert haematopathologists classified 487 consecutive cases of NHL from SAF using the World Health Organization classification, and compared the results to North America (NA) and Western Europe (WEU). Southern Africa had a significantly lower proportion of low-grade (LG) B-NHL (34·3%) and a higher proportion of high-grade (HG) B-NHL (51·5%) compared to WEU (54·5% and 36·4%) and NA (56·1% and 34·3%). High-grade Burkitt-like lymphoma was significantly more common in SAF (8·2%) than in WEU (2·4%) and NA (2·5%), most likely due to human immunodeficiency virus infection. When SAF patients were divided by race, whites had a significantly higher frequency of LG B-NHL (60·4%) and a lower frequency of HG B-NHL (32·7%) compared to blacks (22·5% and 62·6%), whereas the other races were intermediate. Whites and other races had a significantly higher frequency of follicular lymphoma and a lower frequency of Burkitt-like lymphoma compared to blacks. The median ages of whites with LG B-NHL, HG B-NHL and T-NHL (64, 56 and 67 years) were significantly higher than those of blacks (55, 41 and 34 years). Epidemiological studies are needed to better understand these differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| 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".