Distinct characteristics and social determinants in adult T‐cell leukaemia/lymphoma patients at a tertiary cancer centre in Canada
Bibliographic record
Abstract
Adult T-cell leukaemia/lymphoma (ATLL) is a rare, aggressive haematological malignancy linked to human T-cell leukaemia virus type I (HTLV-1) and associated with poor outcomes. Despite its higher prevalence in HTLV-1-endemic regions, the relationship between clinical characteristics and patients' sociocultural background remains underexplored. We retrospectively analysed 79 ATLL patients treated at our institution (1993-2023). The median age at diagnosis was 47 years, and 72% of patients were of Caribbean origin. Median progression-free and overall survival were 10.2 and 16.2 months, respectively, with only five patients receiving allogeneic stem cell transplantation. Central nervous system (CNS) involvement at diagnosis occurred in 22% of patients and was associated with worse outcomes, while 14% experienced CNS relapse within a median of 4.9 months. Using the Ontario Marginalization Index, we found higher levels of material, household/dwelling and racialized/newcomer-related marginalization compared to the general Greater Toronto Area population, though these factors were not linked to poorer outcomes. Our findings reveal that ATLL patients in this cohort were predominantly of Caribbean descent, presented at a young age and faced significant CNS involvement and poor survival outcomes, underscoring ATLL as an unmet clinical need.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".