Chief urology resident management of the urinary tract in stable patients with high spinal cord injuries — survey results and applications in the era of Competence by Design
Bibliographic record
Abstract
INTRODUCTION: The urologist's role in the management of patients with spinal cord injury (SCI) is to prevent upper tract damage and renal failure while facilitating acceptable means for urine elimination. Residency provides the framework to manage SCI patients. The purpose of this study was to determine the surveillance practices of chief urology residents in high SCI patients (T4/5 and above) and their confidence in managing this patient population. METHODS: A 14-question survey was administered at the Canadian chief resident preparation examination in 2017. Questionnaire domains included: visit frequency, imaging modality, laboratory testing, and procedures related to upper and lower tract surveillance. RESULTS: All 33 candidates completed the questionnaire. Chief residents encountered high SCI patients in either diverse clinical settings (48%) or solely as hospital inpatients (33%). Candidates had similar surveillance algorithms for stable high SCI patients. Responses for surveillance cystoscopy in stable high SCI patients varied. When asked how comfortable residents were managing high SCI patients, 42% responded they were comfortable, while the rest responded neutral, uncomfortable, or very uncomfortable. CONCLUSION: Most chief residents made similar surveillance decisions for high SCI patients. Residents did differ on the frequency of cystoscopy and how comfortable they were managing this patient population. In the era of competence by design, this information can be used to highlight training opportunities.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".