A scoping review of the cost-effectiveness of precision treatment in chronic lymphocytic leukemia
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) is a common, incurable leukemia. Precision treatment for CLL uses genetic testing to align therapeutic selection with patient characteristics. Insurers are uneven in their reimbursement of precision CLL treatment, partly due to uncertain evidence of cost-effectiveness. This review surveys the current cost-effectiveness evidence for precision CLL treatment and identifies areas for future research. We conducted a scoping review of economic evaluations of precision CLL treatments indexed in PubMed, Embase, and Web of Science and published by October 2024. Eight articles were retrieved. Studies examined heterogeneous patient populations, treatment regimens, and stratification strategies. Four studies (50%) focused on subgroups with del(17p) and/or TP53 mutations only. Three studies (38%) analyzed the costs and outcomes of both treatment and genetic testing, while 62% did not include the cost or outcomes of genetic testing. All studies obtained clinical model parameters from published trials. Five studies (63%) reported that precision CLL treatment was likely cost-effective at willingness to pay thresholds ranging from $26,489/QALY to $130,477/QALY. Future research should focus on generating real-world data, broadening the scope of analysis to include societal perspectives, and exploring distributional impacts to more effectively address the heterogeneity of precision CLL treatments when determining their cost-effectiveness.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".