What false-negative rates of non-invasive testing are active surveillance patients and uro-oncologists willing to accept in order to avoid prostate biopsy?
Bibliographic record
Abstract
Introduction: Repeat prostate biopsies in active surveillance patients are associated with significant complications. Novel imaging and blood/urine-based non-invasive tests are being developed to better predict disease grade and volume progression. We conducted a theoretical study to determine what test performance characteristics and costs would a non-invasive test(s) require in order for patients and their physicians to comfortably avoid biopsy.Methods: Surveys were administered to two populations to determine an acceptable false-negative rate and cost for such test(s). Active surveillance patients were recruited at time of followup in clinic at Princess Margaret Cancer Centre. Physician members of the Society of Urological Oncology were targeted via an online survey. Participants were questioned about their demographics and other characteristics that might influence chosen error rates and cost.Results: 136 patients and 670 physicians were surveyed, with 130 (95.6%) and 104 (15.5%) responses obtained, respectively. A vast majority of patients (90.6%) were comfortable with a non-invasive test(s) in place of biopsy, with 64.8% accepting a false-negative rate of 5‒20%. Most physicians (93.3%) were comfortable with a non-invasive test, with 77.9% accepting a rate of 5‒20%. Most patients and physicians felt that a cost of less than $1000 per administration would be reasonable.Conclusions: Most patients/physicians are comfortable with a non-invasive test(s). Although a 5% error rate seems acceptable to many, a substantial subset feels that 99% or higher negative predictive value is required. Thus, a personalized approach with shared decision-making between patients and physicians is essential to optimize patient care in such situations.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".