A scoping review of the cost-effectiveness of precision treatment in chronic lymphocytic leukemia
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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