Bibliographic record
Abstract
Introduction The T-cell non-Hodgkin lymphomas are a group of uncommon malignancies that in Western countries account for 15–20% of aggressive lymphomas and between 5% and 10% of all non-Hodgkin's lymphomas (NHL). Within the most current classification schemata, there are 21 distinct diseases that together constitute the mature T- and natural killer (NK)-cell lymphomas, diseases that range from indolent cutaneous lymphomas to aggressive malignancies that are often resistant to routine systemic therapy. Common to many of the types of T-cell lymphoma is a predilection towards extranodal disease, although sites of tropism vary among subtypes. Geographic frequencies of many subtypes, and of T-cell lymphomas as a group, are also highly variable: while only 1.5% of lymphomas are of T-cell lineage in Vancouver, Canada, 18.3% of lymphomas in Hong Kong show a T-cell phenotype. And in Asia, 47.4% of T-cell lymphomas are either NK/T-cell lymphoma, nasal type (NK/T-NT) or adult T-cell leukemia/lymphoma (ATLL), while in North America and Europe, these constitute only 7.1% and 5.3% of T-cell lymphomas, respectively. These variable incidence rates may in part represent differential exposure to risk factors for T-cell lymphoma, including Epstein–Barr virus (EBV) and human T-cell leukemia virus-1 (HTLV-1). As will be explored below, the epidemiologic variety across the subtypes of T-cell lymphoma is matched by the clinical heterogeneity of these diseases, confounding investigation (or understanding) of the class of diseases as a single entity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
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".