Chronic Myeloid Leukemia: who should get a treatment-free trial and how?
Bibliographic record
Abstract
Treatment with a BCR::ABL1 targeted tyrosine kinase inhibitor (TKI) has afforded a near-normal life expectancy for most patients with chronic myelogenous leukemia (CML). Approximately half of CML patients achieve a deep molecular response with TKI therapy and can discontinue treatment. In these patients, the CML can remain in prolonged remission, and patients can experience an improvement in quality of life. The European Leukemia Network (ELN) and the National Comprehensive Cancer Network (NCCN) provide the most up-to-date frameworks for treatment-free trials (TFTs), reflecting best practices from over 13 clinical trials published since the concept first entered the CML vernacular around 2010. Provincial guidelines also exist, such as those published in Quebec by the Groupe Québécois de Recherche en LMC-NMP (the Chronic Myeloid Leukemia and Myeloproliferative Neoplasms Quebec Research Group). The discontinuation of therapy for patients with CML in deep remission marks a potential shift from the management of CML as a chronic illness to the potential for a curative approach to CML. However, for now, only 50% of eligible patients have undergone successful TFT; optimal patient selection and monitoring is required to ensure the best outcomes with such a management strategy.
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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 | 0.056 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".