Bibliographic record
Abstract
PURPOSE OF REVIEW: In the present review, we will define the preleukemic state. We aim at increasing awareness and research in the field of preleukemia that will nurture targeted therapy for the earlier steps of leukemia evolution. RECENT FINDINGS: Emerging evidence supports the role of hematopoietic stem/progenitor cells carrying recurrent leukemia-related mutations as the cell of origin of both myeloid and lymphoid malignancies. The preleukemic stem cells can maintain at least to some extent their functionality; however, they have increased fitness endowed by the preleukemic mutations that lead to clonal expansion. SUMMARY: The latent preleukemic period before overt leukemia presents can take years, and the majority of carriers will never develop leukemia in their lifetime. The preleukemic state is not rare, with greater than 1% of individuals having acquired one or more of the recognized preleukemic lesions. The high frequency of such abnormalities in the population may be the cost of growing old; however, another view could be that in order to survive to old age, the hematopoietic system must adapt to create robust hematopoietic stem/progenitor cells with an increased fitness and clonal expansion. Hence, leukemia does not necessarily start as a disease, but rather as a need, with the normally functioning preleukemic hematopoietic stem cells trying to maintain health for years but in time succumbing to their own acquired virtues.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".