The Reliability of Lymphoma Diagnosis in Small Tissue Samples Is Heavily Influenced by Lymphoma Subtype
Bibliographic record
Abstract
A specific pathologic diagnosis is important in malignant lymphoma because the diverse disease subtypes require tailored approaches to clinical management. Reliance on small samples obtained with cutting needles has been advocated as a less invasive alternative to using larger, excised samples. Although published studies have demonstrated the safety and apparent sufficiency of this approach in informing clinical care, none have systematically determined the accuracy of pathologic lymphoma subtyping based on very small samples. We used a tissue microarray representing 67 cases of malignant lymphoma and 17 samples of nonneoplastic lymphoid tissue to model lymphoma diagnosis in small samples. Overall, 73.8% of the cases were diagnosed with a level of confidence deemed sufficient for directing clinical management; 85.9% of these diagnoses were accurate. Small cell lymphomas with highly distinctive immunophenotypes, including small lymphocytic, mantle cell, and T-lymphoblastic lymphoma, were recognized most consistently and accurately in the small samples. In contrast, follicular lymphoma and marginal zone lymphoma were especially difficult. Our results indicate that the reliability of lymphoma diagnoses based on small samples is heavily influenced by lymphoma subtype.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".