Environmental determinants of leukemia and lymphoma: lessons from African epidemiology and global transition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Childhood leukemia and lymphoma display striking global heterogeneity that cannot be explained by genetic ancestry or diagnostic access alone. African populations, historically characterized by high infectious burden, nutritional stress, and poor sanitation, exhibit a markedly different spectrum of hematologic malignancies from high-income countries, including reduced incidence of common/pre-B acute lymphoblastic leukemia (c-ALL), absence of the early childhood ALL peak, and increased prevalence of Burkitt lymphoma and chloroma-associated acute myeloid leukemia. Drawing on African epidemiologic data and global comparative studies, this review examines how environmental factors across the life course-particularly maternal health, intrauterine exposures, early-life infection, immune programming, and socioeconomic transition-shape leukemogenic pathways. We place these observations in the context of contemporary models of leukemogenesis that recognize prenatal initiation of preleukemic clones with postnatal environmental modulation of disease progression. As low- and middle-income countries undergo rapid epidemiologic transition, understanding how improvements in sanitation, nutrition, and population mixing may alter leukemia incidence is increasingly relevant for prevention strategies. African experience thus provides a natural experiment for elucidating environmental contributions to leukemogenesis with implications extending well beyond the continent.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.016 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 it