Bibliographic record
Abstract
It is well-known that race plays an important role in patient outcomes.As urologists, we see it most clearly in the disproportionate incidence and mortality rates of Black patients with prostate cancer. 1 A similar, though less obvious story, is seen in bladder, penile, and kidney cancer.1,2 Despite these disparities, a recent study found that 96% of patients enrolled in prostate cancer trials between 1987 and 2016 were white.More alarmingly, there was a decrease in the proportion of Black or African American men enrolled in clinical trials between 1995 and 2014.3 Impediments to enrolling Black patients into clinical trials are manifold.A United States systematic review identified five key elements that influence African American participation in oncology clinical trials.4 One of these was the role of health care providers in influencing Black patient participation in clinical trials.Studies have shown that patients who are the same race as their physician have longer clinical encounters that result in improved patient satisfaction.5 As such, increasing the number of Black physicians to build trust and better represent patient populations is a worthwhile endeavour.While the Black population in Canada is significantly smaller than that of the United States, Canada's Black population has doubled between 1996 and 2016, and it continues to grow.6 As the population ages it should come as no surprise if we see a growing number of Black patients succumb to urologic conditions at disproportionate rates compared to their non-Black counterparts.It is our responsibility as urologists to be proactive in creating opportunities for Black trainees and future colleagues who will be well-suited to help address this head on.Recent events, including the deaths of George Floyd in Minnesota and Regis Korchinski-Paquet in Toronto, have once again brought anti-blackness and systemic racism into the public eye.Health care, its institutions, and we as members of them, are not immune to these longstanding societal ills.The urology community in Canada should use this as an opportunity to take stock of where it stands with respect to Black representation in our field.At present, our shortcomings are telling.As of June 2020, there is one Black urology resident across all levels of
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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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.005 |
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".