Standardization of Bone Marrow Reporting for Myelodysplastic Syndromes/Neoplasms on Behalf of the International Consortium for Myelodysplastic Syndromes/Neoplasms
Bibliographic record
Abstract
CONTEXT.—: Standardized bone marrow reporting specifically for myelodysplastic syndromes/neoplasms (MDS) is currently lacking in the literature and much needed in practice. OBJECTIVE.—: To propose a standardized approach to MDS evaluation in bone marrow specimens by (1) enhancing interinstitutional and intrainstitutional collaborations and clinical decision-making among hematopathologists and clinical hematologists and (2) allowing for efficient data extraction for clinical trials, institutional databases, and registry templates. This suggested approach is summarized in a modifiable, user-friendly template for hematopathologists to reference as they examine bone marrows (in the Supplemental Digital Content). DATA SOURCES.—: We built upon the bone marrow template reporting guideline outlined by the College of American Pathologists Pathology and Laboratory Quality Center for Evidence-Based Guidelines and gathered expert insight from hematopathologists and hematologists-oncologists who specialize in MDS. CONCLUSIONS.—: This proposed approach to MDS evaluation in the bone marrow standardizes reporting, which enhances communication among health care professionals and allows for efficient data extraction.
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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 | 0.159 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".