Wait time intervals for patients with prostate cancer in a multidisciplinary rapid diagnostic unit compared to a community-based referral pattern.
Bibliographic record
Abstract
183 Background: Wait times in cancer diagnosis and treatment are important measures of how quickly patients are getting access to care. Prolonged wait times may affect a cancer patient’s treatment outcome, prognosis and quality of life. A previous study conducted in 2003 found the median wait time from suspicion of prostate cancer to radiotherapy (RT) was 247 days. The purpose of this study was to capture wait time intervals for patients referred to and treated with radical RT at the Odette Cancer Centre, comparing patients diagnosed in a rapid diagnostic unit (RDU) versus the usual community referral process. Methods: Patients were screened during RT planning for their newly diagnosed, biopsy-confirmed prostate adenocarcinoma. A semi-structured interview and chart abstraction was conducted in order to obtain the best estimation of key wait time milestones in the care pathway. Results: A total of 87 patients participated in the study: 44 RDU patients and 43 community patients. The median overall wait time interval from suspicion of prostate cancer to RT was 138 days for the RDU cohort and 183 days for the community cohort (p=0.046). There were statistically significant differences between the two cohorts, favouring the RDU cohort, for other key intervals, namely, suspicion to decision-to-treat (DTT; p=0.012), urologist to diagnosis (p=0.0094), diagnosis to DTT (p=0.018), and diagnosis to treatment (p=0.016). Among all patients, 25% perceived a delay in treatment and among those patients, 64% identified a systematic reason for the delay. There was no significant difference on the cause of delay between the RDU and community cohorts (p=0.86). Conclusions: Wait time intervals from suspicion to treatment are significantly shorter for prostate cancer patients in 2011-2 than in 2003 when diagnosed and referred in the community setting. A prostate-specific RDU further reduced wait time intervals supporting more multidisciplinary RDUs for common diseases. However, even in the RDU system, a minority of patients met provincial consensus-based wait time standards. Further work needs to be done to identify why delays are occurring and 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".