Impact of adoption of a decision algorithm including PCA3 for repeat biopsy on the costs for prostate cancer diagnosis in France
Bibliographic record
Abstract
OBJECTIVE: A recent expert study (RAND Appropriateness Method (RAM)) including a panel of 12 European urologists reported that the PCA3 score may be instrumental in taking appropriate prostate biopsy (PBx) decisions, mainly for repeat PBx. This study determined the cost/benefit balance of introducing PCA3 in the decision-making for PBx in France. METHODS: Two RAM models, without and with PCA3, were retrospectively applied to a sample of 808 French men who had PBx in 2010 (78% first, 22% repeat). Outcome measures included the proportion of PBx that could have been avoided (i.e., judged inappropriate) in the French sample according to both RAM models, and the estimated impact of application of these models on the annual number of PBx and associated costs for France (based on most recent published data). RESULTS: Complete profiles were available for 698 men. In the model without PCA3, 2% of PBx were deemed inappropriate. Knowledge of PCA3 would have avoided another 7% of PBx. Repeat PBx would have been avoided in 5% of cases without PCA3 and in 37% with PCA3. For France, application of the RAM model including PCA3 would result in 18,345 fewer repeat PBx. It would be budget-neutral in the unlikely hypothesis of no complications or no costs incurred by complications and would save €1.7 million for a mean cost for complications of €100/procedure or €5 million for a mean cost for complications of €280/procedure, calculated based on US and Canadian data. LIMITATIONS: Limitations of the study are the theoretical nature of the analysis and the fact that PCA3 distributions had to be derived from other sources. CONCLUSIONS: Adoption of RAM expert recommendations including PCA3 for repeat PBx decisions in clinical practice in France would reduce the number of repeat PBx and control costs.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".