Bibliographic record
Abstract
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".