Health Utility Book (HUB)–Cancer: Protocol for a Systematic Literature Review of Health State Utility Values in Cancer
Bibliographic record
Abstract
Background. Treatment options in oncology are rapidly advancing, and public payer systems are increasingly under pressure to adopt new but expensive cancer treatments. Cost-utility analyses (CUAs) are used to estimate the relative costs and effects of competing interventions, where health outcomes are measured using quality-adjusted life years (QALYs). Health state utility values (HSUVs) are used to reflect health-related quality of life or health status in the calculation of QALYs. To support reimbursement agencies in the appraisal of oncology drug submissions, which typically include a CUA component, we have proposed a systematic literature review of published HSUV estimates in the field of oncology. Methods. The following databases will be searched: MEDLINE, EMBASE, EconLit, and CINAHL. A team of reviewers, working independently and in duplicate, will evaluate abstracts and full-text publications for eligibility against broad inclusion criteria. Studies using a direct, indirect, or combination approach to eliciting preferences related to cancer or cancer treatments are eligible. Data extraction will capture details of study methodology, participants, health states, and corresponding HSUVs. We will summarize our findings with descriptive analyses at this stage. A pilot review in thyroid cancer is presented to illustrate the proposed methods. Discussion. This systematic review will generate a comprehensive summary of the oncology HSUV literature. As a component of the Health Utility Book (HUB) project, we anticipate that this work will assist both health economic modelers as well as critical reviewers in the development and appraisal of CUAs in oncology.
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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 | 0.089 | 0.139 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.121 | 0.012 |
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".