Bibliographic record
Abstract
The article by Michael Leveridge and colleagues1 raises an issue that is not often addressed in the urology literature, and should provoke some discomfort and discussion. Every physician involved in resident training and evaluation is aware that, about 10 years ago, the Royal College redefined the goals of training beyond the acquisition of expertise in a specialty. Six additional roles were added to that of medical expert, including health advocate, communicator, collaborator, manager, scholar and professional. Resident evaluations now require the resident's performance in each of these areas to be determined on a regular basis. For residents working hard to master surgical and clinical skills and basic and clinical sciences, the challenge represented by the acquisition of expertise in these other 6 roles, and (perhaps even more critically) the need to demonstrate that the skill sets involved have been acquired, is large. One of these roles, the scholar (i.e., researcher), has been accepted for 100 years as an important one in surgery. Canadian residents are encouraged to obtain some research experience, and indeed this a requirement of some programs. However, it is likely that most urologists involved in resident training believe that the teaching involved in the 5 roles beyond medical expert and scholar occurs implicitly, by example, rather than by explicit instruction. We teach communication skills by being good communicators; we demonstrate collaborator skills by collaborating; and so on. The article by Leveridge and colleagues confirms that explicit training in health advocacy is rare in residency training and that active participation in health advocacy projects is virtually nonexistent. The Royal College presumably considers that this is a deficiency. But is it? Becoming a medical expert involves the acquisition of multiple skill sets. Five years of residency training after medical school is a short time to develop proficiency. Would formal training in the other 5 roles, beyond expert and scholar, represent a dilution of the focus on knowledge acquisition and clinical and surgical skills? Or would resident instruction and opportunity for involvement in, for example, health advocacy, raise the level of expertise achieved in other areas? Does this matter? We welcome our readers' views on this question. The Laval group continues their remarkable productivity in the area of cancer biomarkers. Stephan Bolduc and colleagues2 report that urinary prostate specific antigen (PSA), and particularly the urinary to serum PSA ratio, discriminate between benign prostatic hyperplasia and prostate cancer in men with mild PSA elevation. Urinary PSA is appealing because of its availability and low cost. It would have been interesting to know how urinary PSA performs compared with free versus total ratio, or to a multi-parameter nomogram approach incorporating other risk factors. We look forward to more evidence regarding the utility of this assay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".