Treatment Outcomes in Chronic Myeloid Leukemia: Does One Size Fit All?
Bibliographic record
Abstract
With the success of tyrosine kinase inhibitors (TKIs) in achieving next-to-normal overall survival in chronic myeloid leukemia (CML), treatment-free remission (TFR) has become a significant goal in the management of this disease. Discontinuation of therapy is attractive to both patients and physicians because maintaining a stable BCR-ABL transcript level without therapy would imply true operational CML cure. With TFR, patients are not exposed to unknown long-term adverse effects of TKIs and common adverse effects that may affect quality of life. Several factors need to be considered before attempting TFR, because this goal is not appropriate for a significant proportion of patients with CML. Patient-related factors, CML response to therapy and its duration, monitoring capacity, patient preferences and compliance with monitoring, and economic factors influence the decision to attempt to discontinue TKIs. Unfortunately, only 50% of patients are appropriate candidates for discontinuation of treatment. Of those, another 50% maintain stable disease while off TKIs. This means that merely 25% of patients achieve TFR. Further optimization and research are required to be able to extend this treatment goal to a larger population of patients. Although TFR is attractive and desirable, this goal is not a one-size-fits-all approach, and we should continue to focus on patients with CML having a normal OS with the best quality of life possible.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".