Budget impact analysis of treatment‐free remission in nilotinib‐treated Japanese chronic myeloid leukemia patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Treatment-free remission (TFR), in which patients discontinue pharmacotherapy and remain in molecular remission, is an emerging treatment goal for patients with chronic myeloid leukemia (CML). Attainment of TFR requires an increased frequency of molecular monitoring, to ensure that patients maintain a deep molecular response. The objective of this analysis was to assess the economic impact of stopping nilotinib among Japanese TFR-eligible patients. A Markov model evaluated the economic impact of TFR among the study population, TFR-eligible CML patients diagnosed since 2012. The model compared patients who had discontinued tyrosine kinase inhibitor (TKI) treatment (ie, attempted TFR) with patients that continued TKI treatment. A 3-y time horizon was modeled from a Japanese public payer perspective. Costs associated with drug treatment, hospital/physician visits, and molecular monitoring were considered. TFR-eligible patients were calculated from Japanese CML incidence rates and efficacy was derived from nilotinib trials. Japanese co-payment maximums were utilized to assess the patient perspective. An estimated 761 and 140 patients were eligible for first- and second-line nilotinib, respectively, in 2019. Assuming that 100% of eligible patients complied, TFR was associated with cost savings of ¥7 625 174 640 (US$66 567 775) over 3 y. In scenarios with reduced willingness to attempt TFR, cost savings persisted. Achievement of TFR was estimated to markedly reduce out-of-pocket expenses for CML patients, regardless of the timing of relapse. Stopping nilotinib for TFR-eligible patients in Japan may result in significant cost savings to both payers and patients. Monitoring costs contributed little to overall annual costs and decreased over time.
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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 | 0.001 | 0.006 |
| 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 it