State-of-the-art in the management of chronic myelogenous leukemia in the era of the tyrosine kinase inhibitors: evolutionary trends in diagnosis, monitoring and treatment
Bibliographic record
Abstract
The treatment of patients with chronic myeloid leukemia (CML) continues to evolve rapidly as we gain better insights into the best monitoring strategies and as there is experience with the second generation tyrosine kinase inhibitors (TKI). Certain observations about CML and its clinical course remain relevant, such at its triphasic course and the prognostic value of the Sokal and Hasford scores. Other aspects of the disease including the most appropriate clinical monitoring and follow-up strategies and indications for changing therapy are evolving more rapidly. Best practice recommendations for monitoring of response have not only evolved over time but also affected by the availability and reliability of standard cytogenetics, FISH and molecular monitoring. Standard dose imatinib remains the best first-line therapy for most patients with first chronic phase CML. Patient and disease-related factors to evaluate when considering alternatives such as higher doses of imatinib, dasatinib, nilotinib and allogeneic transplant are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".