Dasatinib dose optimisation based on therapeutic drug monitoring reduces pleural effusion rates in chronic myeloid leukaemia patients
Bibliographic record
Abstract
Dasatinib is a second-generation BCR-ABL1 tyrosine kinase inhibitor approved for patients with chronic myeloid leukaemia (CML). Dasatinib 100 mg per day is associated with an increased risk of pleural effusion (PlEff). We randomly evaluated whether therapeutic drug monitoring (TDM) may reduce dasatinib-associated significant adverse events (AEs) by 12 months (primary endpoint). Eligible patients started dasatinib at 100 mg per day followed by dasatinib (C)min assessment. Patients considered overdosed [(C)min ≥ 3 nmol/l) were randomised between a dose-reduction strategy (TDM arm) and standard of care (control arm). Out of 287 evaluable patients, 80 patients were randomised. The primary endpoint was not met due to early haematological AEs occurring before effective dose reduction. However, a major reduction in the cumulative incidence of PlEff was observed in the TDM arm compared to the control arm (4% vs. 15%; 11% vs. 35% and 12% vs. 39% at one, two and three years, respectively (P = 0·0094)). Molecular responses were superimposable in all arms. Dasatinib TDM during treatment initiation was feasible and resulted in a significant reduction of the incidence of PlEff in the long run, without impairing molecular responses. (NCT01916785; https://clinicaltrials.gov).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".