Continuing towards optimization of bladder cancer care in Canada: Summary of the 3rd BCC-CUA-CUOG bladder cancer quality of care consensus meeting
Bibliographic record
Abstract
3rd BCC-CUA-CUOG bladder cancer quality of care consensus meetingIn early 2016, a Canadian multidisciplinary committee published a white paper entitled "Recommendations for the improvement of bladder cancer quality of care in Canada: A consensus document reviewed and endorsed by Bladder Cancer Canada (BCC), Canadian Urologic Oncology Group (CUOG), and Canadian Urological Association (CUA)". 1 This was a summary and report of the committee's consensus deliberations during the first twoday BCC-CUA-CUOG Bladder Cancer Quality of Care Meeting (BCQCM) held in late 2014.One of the recommendations from the report was to perform a Delphi process to establish a set of quality indicators across important categories of bladder cancer care. 1 This process was undertaken, and led to a 2017 publication listing 60 quality indicators for consideration. 2 In November 2016, another multidisciplinary committee consisting largely of the same members met at a second BCQCM, which focused on the patient journey and optimizing management.The report of this second BCQCM was published in 2018.3 The following is a summary of the third national BCQCM.The objectives for the meeting were the following:-To provide an update on the status of the Canadian Bladder Cancer Information System (CBCIS) and its potential future impact; -To set benchmarks for the core quality indicators selected at the 2 nd BCQCM; -To discuss the desirability and feasibility of an annual Canadian Bladder Cancer Forum; -To review barriers and enablers of bladder preservation for invasive bladder cancer; -To examine and discuss the future of bladder cancer research, with a focus on patient engagement and their priorities; and -To review issues and concerns from the patient perspective and Bladder Cancer Canada.
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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 | 0.043 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".