Precision of Histological Bone Marrow Staging in Follicular Lymphoma and Diffuse Large B-cell Lymphoma
Bibliographic record
Abstract
INTRODUCTION: In Non-Hodgkin Lymphoma (NHL), bone marrow histology is the gold standard against which ancillary investigations such as immunophenotyping and gene rearrangement studies are interpreted. There is currently no data on the reproducibility of histological findings. This study was conducted to determine the rates of inter- and intra-observer agreement in histological detection of bone marrow involvement in the two major subtypes of NHL, Diffuse Large B-cell Lymphoma (DLBCL), and Follicular Lymphoma (FL). METHODS: The bone marrow slides of randomly selected DLBCL and FL cases were independently examined by two hematologists using standardized reporting criteria on two occasions at least two weeks apart. Samples included both aspirate and trephine biopsy slides. Weighted kappa statistics were used to examine agreement for the discrete measures. RESULTS: Weighted kappa analyses showed variable inter-observer agreement in 38 DLBCL cases [aspirate=0.52; trephine= 0.77] and 38 FL cases [aspirate=0.48; trephine=0.77]. CONCLUSION: Overall, higher agreement rates were noted with trephine biopsies than with aspirates. Except for the high intra-observer agreement on trephine biopsy assessment in FL, there is poor agreement in histological staging of both FL and DLBCL which demonstrates the limitations of histological diagnosis and the futility of interpreting ancillary tests against histology.
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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 | 0.029 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".