Diagnostic assessment program for prostate cancer: Lessons learned after 2 years and degree of compliance to Canadian guidelines
Bibliographic record
Abstract
BACKGROUND: In 2018, our Institute launched the Diagnostic Assessment Program (DAP) for prostate cancer. It enabled quick access to a urologist for patients presented to family physician with elevated PSA and allowed fast multidisciplinary patient care. We aim to document our data over 2 years in comparison to data before implementation of DAP and its impact on the degree of adherence to Canadian guidelines. METHODS: From April 2016 to April 2020, 880 patients who were evaluated for prostate cancer at Thunder Bay Regional Health Sciences Centre (TBRHSC) were included in this study. Patients' characteristics, clinical data, waiting times and line of treatment before and after implementation of DAP were calculated and statistically analysed. RESULTS: The median waiting time to urology consultation was significantly reduced from 68 (IQR 27-168) days to 34 (23-44) days (p < 0.001). The time from patient's referral to prostate biopsy decreased substantially from 34 (20-66) days to 18(11- 25) days after DAP (p < 0.001). After DAP, the percentage of Gleason 6 detected prostate cancers were significantly increased (19.7% to 30%) (p = 0.02). After DAP, rate for intermediate-risk patients elected for external beam radiotherapy (from 53.5% to 57.9%, p = 0.53) and radical prostatectomy (from 34.5% to 39.4%, p = 0.47) increased. More compliance to Canadian guidelines was observed in intermediate risk patients (88% vs 97.3%, p =.008). CONCLUSIONS: Implementation of DAP has led to a notable reduction of waiting time to urology consult and prostate biopsy. There is significant increase in Gleason 6 detected prostate cancer. Increased compliance to Canadian guidelines was detected in intermediate risk patients.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".