Improved Wait Time Intervals for Prostate Cancer Patients in a Multi-Disciplinary Rapid Diagnostic Unit Compared to a Community-Based Referral Pattern
Bibliographic record
Abstract
BACKGROUND: Wait times in cancer diagnosis and treatment may significantly affect a patient's treatment outcome, prognosis and quality of life. The purpose of this study was to capture wait time intervals for patients with prostate cancer treated with radiotherapy (RT) at the Odette Cancer Centre, Toronto, Ontario, Canada and to compare patients diagnosed in a rapid diagnostic unit (RDU) versus the usual community referral process. METHODS: Patients agreed to participate in the study during their RT planning sessions. A semi-structured interview and chart abstraction was conducted to record key wait time milestones. RESULTS: A total of 87 patients participated in the study: 44 RDU patients and 43 community patients. The median overall wait time intervals from suspicion of prostate cancer to RT was 138 and 183 days, respectively (p = 0.046). There were statistically significant differences observed for other key wait time intervals favouring the RDU cohort: suspicion to decision-to-treat (DTT; p = 0.012), urologist visit to diagnosis (p = 0.0094), diagnosis to DTT (p = 0.018), and diagnosis to treatment (p = 0.016). Risk category and Gleason sum was independently predictive of longer intervals from diagnosis to DTT. INTERPRETATION: Wait time intervals from suspicion to treatment are significantly shorter for prostate cancer patients in 2011 to 2012 than in 2003 when patients were diagnosed and referred in the community setting. A prostate-specific RDU further reduced a number of key wait time intervals supporting more multidisciplinary RDUs for common diseases. Further work needs to be done to identify why delays are occurring and to develop new processes to minimize delays.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".