Molecular/Cytogenetic Education for Hematopathology Fellows
Bibliographic record
Abstract
OBJECTIVES: At a discussion on molecular/cytogenetic education for hematopathology fellows at the 2018 Society for Hematopathology Program Directors Meeting, consensus was that fellows should understand basic principles and indications for and limitations of molecular/cytogenetic testing used in routine practice. Fellows should also be adept at integrating results of such testing for rendering a final diagnosis. To aid these consensus goals, representatives from the Society for Hematopathology and the Association for Molecular Pathology formed a working group to devise a molecular/cytogenetic curriculum for hematopathology fellow education. CURRICULUM SUMMARY: The curriculum includes a primer on cytogenetics and molecular techniques. The bulk of the curriculum reviews the molecular pathology of individual malignant hematologic disorders, with applicable molecular/cytogenetic testing for each and following the 2017 World Health Organization classification of hematologic neoplasms. Benign hematologic disorders and bone marrow failure syndromes are also discussed briefly. Extensive tables are used to summarize genetics of individual disorders and appropriate methodologies. CONCLUSIONS: This curriculum provides an overview of the current understanding of the molecular biology of hematologic disorders and appropriate ancillary testing for their evaluation. The curriculum may be used by program directors for training hematopathology fellows or by practicing hematopathologists.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".