Abstract 96: When Canada is a Under-Resourced Country: Patient-Oriented Research on Optimizing Rational Decision-Making for Bladder Cancer
Bibliographic record
Abstract
Abstract Purpose: To identify gaps between the ideal and current approaches to Non-Muscle Invasive Bladder Cancer (NMIBC) in Canada, and determine what should be done short- and long-term to address those gaps. Methods: Prompted by a patient diagnosed with NMIBC in July 2022, we first searched the global literature for the most-effective preventive, screening, diagnostic, and therapeutic approaches to NMIBC, and also examined sites such as Bladder Cancer Canada and professional societies’ sites for clinical recommendations. We next conducted written and oral interviews with urologists, researchers, primary care physicians, and manufacturers of related interventions to understand and synthesize the literature and determine the best clinical course of action. Results: Our approach produced prompt and free care, of quality ranging from poor to outstanding. It required substantial patient resilience and advocacy, much of which is likely easily remediable, some of which will require policy changes. Care quality gaps of particular concern that were identified included: Conclusion: Treatment of NMIBC in Canada is not currently provided in an optimized or rational fashion. Several improvements that are without meaningful associated cost have been identified that we conclude should be immediately implemented. Other interventions should be considered immediately by policymakers to reduce unnecessary morbidity, mortality, suffering, and other societal costs. Citation Format: Erica Frank. When Canada is a Under-Resourced Country: Patient-Oriented Research on Optimizing Rational Decision-Making for Bladder Cancer [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 96.
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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 | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".