Impact of low-grade adverse events on health-related quality of life in adult patients receiving imatinib or nilotinib for newly diagnosed Philadelphia chromosome positive chronic myelogenous leukemia in chronic phase
Bibliographic record
Abstract
OBJECTIVE: Chronic myeloid leukemia (CML) treatment relies on tyrosine kinase inhibitors (TKIs), but their use can be associated with low-grade adverse events (AEs). This analysis aimed to identify the low-grade AEs which significantly impact the Health Related Quality of Life (HRQoL) of CML patients in chronic phase (CP) and to compare the incidence of such AEs among nilotinib- and imatinib-treated patients. RESEARCH DESIGN AND METHODS: Data from the 48 month ENESTnd trial were used (N = 593 patients). HRQoL was assessed using generic (SF-36) and leukemia-specific (FACT-Leu) HRQoL surveys. AEs were categorized into 26 system organ classes. RESULTS: In the adjusted regression model, five low-grade AE categories - gastrointestinal disorders, blood and lymphatic system disorders, general disorders and administration site conditions, musculoskeletal disorders, and psychiatric disorders - significantly impaired at least one HRQoL score. The incidence rate of these five AE categories was either significantly lower for nilotinib than imatinib or not different between the two drugs. The AE categories with lower incidence for both nilotinib 300 mg BID and 400 mg BID versus imatinib 400 mg daily were gastrointestinal, blood and lymphatic system, and musculoskeletal; nilotinib 300 mg BID had lower incidence than imatinib for general disorders. LIMITATIONS: Low-grade AEs were grouped and analyzed by system organ class category, so the effect of some rare individual AEs on HRQoL may have been missed. CONCLUSIONS: The impact of low-grade AEs on HRQoL should be taken into account, along with other factors, when selecting the optimal treatment for patients newly diagnosed with CML-CP.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".