Proposed classification of lymphoid neoplasms for epidemiologic research from the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph)
Bibliographic record
Abstract
Recent evidence suggests that there is etiologic heterogeneity among the various subtypes of lymphoid neoplasms. However, epidemiologic analyses by disease subtype have proven challenging due to the numerous clinical and pathologic schemes used to classify lymphomas and lymphoid leukemias over the last several decades. On behalf of the International Lymphoma Epidemiology Consortium (InterLymph) Pathology Working Group, we present a proposed nested classification of lymphoid neoplasms to facilitate the analysis of lymphoid neoplasm subtypes in epidemiologic research. The proposed classification is based on the World Health Organization classification of lymphoid neoplasms and the International Classification of Diseases-Oncology, Third Edition (ICD-O-3). We also provide a translation into the proposed classification from previous classifications, including the Working Formulation, Revised European-American Lymphoma (REAL) classification, and ICD-O-2. We recommend that epidemiologic studies include analyses by lymphoma subtype to the most detailed extent allowable by sample size. The standardization of groupings for epidemiologic research of lymphoma subtypes is essential for comparing subtype-specific reports in the literature, harmonizing cases within a single study diagnosed using different systems, as well as combining data from multiple studies for the purpose of pooled analysis or meta-analysis, and will probably prove to be critical for elucidating etiologies of the various lymphoid neoplasms.
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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 | 0.033 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".