Inherited predisposition to haematopoietic malignancies: overcoming barriers and exploring opportunities
Bibliographic record
Abstract
Inherited predisposition to haematopoietic malignancies, due to deleterious germline variants in a variety of genes, is an important clinical entity with implications for the health and management of patients and their family members. Unfortunately, there remain several common misconceptions in this field that can result in patients going unrecognised and/or having incomplete or improper testing including: the impression that inherited haematological malignancy syndromes are rare, that myeloid and lymphoid malignancy predisposition syndromes are mutually exclusive, and that solid tumour predisposition syndromes are unique and distinct from haematopoietic malignancy predisposition syndromes. In the present review, we challenge these ideas with our insights into germline genetic testing for these conditions with the hope that increased awareness and knowledge will overcome barriers and lead to improved diagnosis and management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".