Health Economic Evidence for Liquid- and Tissue-based Molecular Tests that Inform Decisions on Prostate Biopsies and Treatment of Localised Prostate Cancer: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Several liquid- and tissue-based biomarker tests (LTBTs) are available to inform the need for prostate biopsies and treatment of localised prostate cancer (PCa) through risk stratification, but translation into routine practice requires evidence of their clinical utility and economic impact. OBJECTIVE: To review and summarise the health economic evidence on the ability of LTBTs to inform decisions on prostate biopsies and treatment of localised PCa through risk stratification. EVIDENCE ACQUISITION: A systematic search was performed in the EMBASE, MEDLINE, Health Technology Assessment, and National Health Service Health Economic Evaluation databases. Eligible publications were those presenting health economic evaluations of an LTBT to select individuals for biopsy or risk-stratify PCa patients for treatment. Data on the study objectives, context, methodology, clinical utility, and outcomes were extracted and summarised. EVIDENCE SYNTHESIS: = 3). Most (18 of 22) studies concluded that biomarker tests could decrease health care costs or would be cost-effective. However, downstream consequences and long-term outcomes were typically not included in studies that evaluated LTBT to inform biopsies. Long-term effectiveness was modelled by linking evidence from different sources instead of using data from prospective studies. CONCLUSIONS: Although studies concluded that LTBTs would probably be cost-saving or -effective, the strength of this evidence is disputable because of concerns around the validity and transparency of the assumptions made. This warrants prospective interventional trials to inform health economic analyses to ensure collection of direct evidence of clinical outcomes based on LTBT use. PATIENT SUMMARY: We reviewed studies that evaluated whether blood, urine, and tissue tests can reduce the health and economic burden of prostate cancer. Results indicate that these tests could be cost-effective, but clinical studies of long-term outcomes are needed to confirm the findings.
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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 | 0.020 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".