Hospital encounters and associated costs of prostate evaluation for clinically important disease
Bibliographic record
Abstract
INTRODUCTION: Systematic transrectal ultrasonography (TRUS) biopsy has been the standard diagnostic tool for prostate cancer (PCa) but is subject to limitations, such as a high false-negative rate of cancer detection. Multiparametric magnetic resonance imaging (mpMRI) prior to biopsy is emerging as an alternative diagnostic procedure for PCa. The PRECISE study found that MRI followed by a targeted biopsy was more accurately able to identify clinically significant cancer than TRUS biopsy. METHODS: PRECISE study patients recruited in Ontario between January 2017 and November 2019 were linked to various Ontario provincial administrative databases available at the Institute for Clinical and Evaluative Sciences (ICES ) to determine health resources used, associated costs, and hospitalizations in the 14 days after biopsy. Costs are presented in 2021 CAD. RESULTS: A total of 281 males were included in this study, with 48.4% of the patients in the TRUS biopsy group, 28.1% in the MRI+, and 23.5% in the MRI- group. Twenty-one patients (15%) from the TRUS biopsy group were seen at a hospital in the 14 days after their biopsy compared to fewer than five patients (6%) from the MRI+ group. The mean per person per year (PPPY) costs for the TRUS and all MRI groups (MRI- and MRI+) were $7828 and $8525, respectively. CONCLUSIONS: Patients in the TRUS biopsy group experienced more hospital encounters compared to patients who received an MRI prior to their biopsy. This economic analysis suggests that MRI imaging prior to biopsy is not associated with a significant increase in costs.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".