Diagnosis, referral, and primary treatment decisions in newly diagnosed prostate cancer patients in a multidisciplinary diagnostic assessment program
Bibliographic record
Abstract
INTRODUCTION: We aimed to report on data from the multidisciplinary diagnostic assessment program (DAP) at the Gale and Graham Wright Prostate Centre (GGWPC) at North York General Hospital (NYGH). We assessed referral, diagnosis, and treatment decisions for newly diagnosed prostate cancer (PCa) patients as seen over time, risk stratification, and clinic type to establish a deeper understanding of current decision-making trends. METHODS: From June 2007 to April 2012, 1277 patients who were diagnosed with PCa at the GGWPC were included in this study. Data was collected and reviewed retrospectively using electronic patient records. RESULTS: 1031 of 1260 patients (81.8%) were seen in a multidisciplinary clinic (MDC). Over time, a decrease in low-risk (LR) diagnoses and an increase intermediate-risk (IR) diagnoses was observed (p<0.0001). With respect to overall treatment decisions 474 (37.1%) of patients received primary radiotherapy, 340 (26.6%) received surgical therapy, and 426 (33.4%) had conservative management; 57% of patients who were candidates for active surveillance were managed this way. No significant treatment trends were observed over time (p=0.8440). Significantly, different management decisions were made in those who attended the MDC compared to those who only saw a urologist (p<0.0001). CONCLUSIONS: In our DAP, the vast majority of patients presented with screen-detected disease, but there was a gradual shift from low- to intermediate-risk disease over time. Timely multidisciplinary consultation was achievable in over 80% of patients and was associated with different management decisions. We recommend that all patients at risk for prostate cancer be worked up in a multi-disciplinary DAP.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".