Systematic Literature Review on Economic Evaluations and Health Economic Models in Metastatic Castration-Sensitive Prostate Cancer
Bibliographic record
Abstract
At diagnosis, metastatic prostate cancer (PC) is sensitive to androgen deprivation therapy (ADT), and patients are usually referred to as having castration-sensitive prostate cancer (mCSPC). The combination of ADT and androgen receptor pathway inhibitors (ARPI) is the current standard of care for mCSPC. This study aimed to review the literature on economic evaluations and health economic models related to mCSPC. A literature search was performed covering Medline, Embase, and Scopus with additional grey literature sources. Studies with data on health economic evaluations focusing on Europe or North America were relevant. 18 peer-reviewed articles and 10 grey literature documents were included. The majority (n = 23) had a deterministic Markov structure and applied either Markov cohort or partitioned survival models. Evaluations investigated various types of ADT-based combinations, comparing the addition of ARPI, chemotherapy agents, or radiation therapy to ADT alone. We concluded that economic evaluations in the field of PC are widely published, and there are a large number of publications even in the specific subgroup of mCSPC. Regardless of the investigated interventions, most studies applied similar methodologies and simulated patients from the mCSPC state until the development of mCRPC or death.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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 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".