Comment on: “Switching TKIs during CML therapy is frequent, mostly driven by intolerance, and does not affect survival: a prospective Quebec registry study” by Busque et al.
Bibliographic record
Abstract
I have read with great interest the study by Busque et al. [ 1 ], in which the authors evaluated the real-world data on switching patterns of tyrosine kinase inhibitors (TKIs) among a large population of chronic myeloid leukemia (CML) patients in Quebec, Canada. This study is very valuable since data on this topic is still limited in the literature, however, there still some points that need to be further underlined. The study reports that nearly half of the patients were switched to second-line TKI therapy with a median follow-up of approximately 6 years. The most common reason for switching was TKI-associated toxicities [ 1 ]. In the observational SIMPLICITY study, the proportion of patients with a TKI switch was lower than that observed in the study of Busque and colleagues, with the percentages of patients who were switched of an alternative TKI for the first and second years of therapy of 17.8 and 9.5%, respectively [ 2 ]. The update of the same study among European patients showed that, with a median follow-up of 5 years, 25% of patients were switched to second-line TKI therapy [ 3 ]. Similarly, intolerance was the most common reason for switching in the SIMPLICITY study. In the UK TARGET CML study, with a median follow-up of nearly 3 years, 44% of the patients were switched from first-line TKI [ 4 ], which was comparable to that of the Quebec registry [ 1 ]. In this study, resistance was the most common reason for switching, observed in 65% of the patients [ 4 ]. In patients with CML experiencing grade 4 or persisting grade 3 adverse events (AEs) under TKIs, the recommendation is to switch to an alternative treatment [ 5 , 6 ]. As the distribution of toxicity grades were not shared in the study of Busque et al. [ 1 ], maybe not all patients with a TKI switch due to intolerance had grade ≥3 AE. Generic TKIs are now globally available [ 7 , 8 ], and the rates of toxicities may differ between generics and the original molecule as well as between different generics [ 7 , 9 ]. In a previously published study also coming from Quebec, Canada, it was shown that switching to another TKI was higher in patients receiving generic imatinib when compared to those with branded imatinib and intolerance was the main reason for this higher non-persistence [ 10 ]. There was no data on the use of generics in the study of Busque et al. [ 1 ], and it would be interesting to see this information, together with the comparative data between generics and branded TKIs regarding rates of switching and other outcomes.
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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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.031 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".