Stopping Study in Chronic Myeloid Leukemia: Defining a New Paradigm for Ontario
Bibliographic record
Abstract
Objectives: We aim to test a new paradigm for safely stopping tyrosine kinase inhibitor (TKI) treatment in chronic myeloid leukemia (CML) patients by monitoring molecular response every 6 weeks for 6 months instead of the recommended 4-week intervals. Methods: A prospective pilot cohort study was conducted to assess the outcome of cessation of TKI treatment in chronic-phase CML patients. Patients' polymerase chain reaction (PCR) for BCR-ABL was tested every 6 weeks for 36 weeks to ensure ongoing major molecular response (MMR). Withdrawal syndrome, psychological effects, and quality of life as per the EORTC Global Quality of Life (QoL) scoring system were assessed during each visit every 6 weeks for 36 weeks. Results: 18 consenting patients were enrolled, 3 were eliminated from data analysis as additional time is required to assess their molecular responses after stopping. 12 of the 15 enrolled participants were able to successfully stop TKI therapy and remain in remission, yielding an 80% success rate in safely stopping TKI therapy. 3 patients had relapsed and successfully regained MR with resumption of TKI therapy. Conclusion: According to literature, the successful stopping rate in CML is 50%. The 80% success rate may be attributed to the longer duration of treatment and being a first stopping study opportunity for patients in Windsor. Limitations of this study include a small sample size. We hope to expand this study in Ontario and test this real-world paradigm of testing CML patients who stop their TKI every 6 weeks, in larger populations in the future.
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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 | 0.049 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".