Hematopathology Approaches to Diagnosis and Prognosis of Indolent B-Cell Lymphomas
Bibliographic record
Abstract
The advent of new technologies has contributed to improvements in the diagnosis and classification of the non-Hodgkin lymphomas (NHL). Use of a more extensive test menu of paraffin active monoclonal antibodies for immunohistochemistry, molecular cytogenetic studies including standard cytogenetics, multi-color fluorescence in-situ hybridization (FISH), polymerase chain reaction and locus-specific FISH, as well as developments in high-resolution techniques including microarray gene expression profiling and array comparative genomic hybridization (CGH) allow more accurate diagnosis and precise definition of biomarkers of value in risk stratification. The identification of disease-specific gene lists resulting from expression profiling provides a number of potential protein targets that can be validated using immunohistochemistry. We will highlight how improvements in our understanding of lymphoma biology rapidly facilitate the development of new diagnostic reagents that could be used to alter clinical practice. These changing trends allow the development of new diagnostic strategies used to render accurate sub-classification of entities within the category of indolent B-cell lymphomas, including their distinction from related but more aggressive disorders, such as mantle cell lymphoma. A comprehensive understanding of the biology of these distinct lymphoid tumors will allow us to identify novel disease-related genes and should facilitate the development of improved diagnostics, outcome prediction, and personalized approaches to treatment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".