Comparison of Referring and Final Pathology for Patients With Non-Hodgkin's Lymphoma in the National Comprehensive Cancer Network
Bibliographic record
Abstract
PURPOSE: Before the implementation of the WHO lymphoma classification system, disagreement about pathologic diagnosis was common. We sought to estimate the impact of expert review in the modern era by comparing final pathologic diagnoses at five comprehensive cancer centers with diagnoses assigned at referring centers. PATIENTS AND METHODS: Patients in the National Comprehensive Cancer Network (NCCN) non-Hodgkin's lymphoma (NHL) database with a documented pathologic diagnosis before presentation and a final pathologic diagnosis of any of five common B-cell NHLs were eligible. After central review of discordant cases, we estimated the rate of pathologic concordance, then investigated the etiology of discordance as well its potential impact on prognosis and treatment. RESULTS: The overall pathologic discordance rate was 6% (43 of 731 patients; 95% CI, 4% to 8%). For the majority of cases in which the referring diagnosis was apparently final, no additional studies were conducted at the NCCN center, and the change in diagnosis reflected a different interpretation of existing data. Concordance was highest for diffuse large B-cell lymphoma (95%) and follicular lymphoma (FL; grades 1, 2, and not otherwise specified, 95%) and lowest for grade 3 FL (88%). Of the 43 pathologically discordant cases, 81% (35 patients) might have experienced a change in treatment as a result of the pathologic reclassification. CONCLUSION: In the era of the WHO lymphoma classification system, the majority of common B-cell NHLs diagnosed in the community were unchanged by second opinion review by an expert hematopathologist. However, for one patient in 20, there was a discordance in diagnosis that could have altered therapy.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".