Patient preferences for stopping tyrosine kinase inhibitors in chronic myeloid leukemia
Bibliographic record
Abstract
BACKGROUND: We used an interview-assisted survey of patients with chronic myeloid leukemia (cml) at a single tertiary care centre to explore patient reactions to and preferences for, and the risk-acceptability of, stopping tyrosine kinase inhibitor (tki) treatment. METHODS: The study included patients with confirmed cml currently being treated with a tki. The survey was conducted by structured interview using a standard form. Patient preferences were explored in a case-based scenario using 0%-100% visual analog scales and 5-point Likert scales. Data were analyzed using proportions for dichotomous variables and medians and interquartile ranges for continuous variables. RESULTS: Of 63 patients approached, 56 completed the survey. Participant responses suggest that the idea of stopping tki use is appealing to many patients if there is a chance of long-term stable disease and a high probability of response upon restarting a tki. Participants were more likely to stop their tki as the risk of relapse decreased. Participants reported loss of disease control and failure of disease to respond to treatment as important concerns if they chose to stop their tki. CONCLUSIONS: Given the current 60% estimated rate of relapse after discontinuation of tki therapy, most patients with cml chose to continue with tki. However, at the lower relapse rates reported with second-generation tkis, participants were more undecided, demonstrating a basic understanding of risk. Contrary to our hypothesis, neither compliance nor occurrence of side effects significantly affected patient willingness to stop their tki.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".