Exposure to radiation and medical oncology training: A survey of Canadian urology residents and fellows
Bibliographic record
Abstract
INTRODUCTION: Residency experiences and teaching in oncology among urology residents are variable across Canada. We sought to identify how radiation and medical oncology concepts, as they pertain to genitourinary malignancies, are taught to urology residents. METHODS: A total of 190 trainees enrolled in Canadian urology residency training programs were invited to participate in the study from January 2016 to June 2016. Participants completed an online questionnaire addressing the training they received. RESULTS: The overall response rate was 32%. Twenty-three percent of respondents were in their fellowship year; 17%, 20%, 10%, 17%, and 12% were first-, second-, third-, fourth-, and fifth-year residents, respectively, with a median of four (range 1-9) respondents from each training program. Ninety-five percent of respondents had academic half-day (AHD) as part of their training that included radiotherapy (61%) and chemotherapy (51%) teaching. Most respondents indicated their main exposure to chemotherapy and radiation came from informal teaching in urology clinics. Twenty-nine percent and 41%, of participants had mandatory rotations in radiation and medical oncology, respectively. Only 6% of respondents used their voluntary elective time in these disciplines and most voluntary electives were of 1-2-week duration. Despite this, 90% of respondents preferred some mandatory radiation and medical oncology training. CONCLUSIONS: Most of the limited exposure that urology residents have to medical and radiation oncology is through AHD or informal urology clinics, despite a desire among current urology trainees to have clinical exposure in these areas. Moving forward, urology residency programs should consider integrating medical and radiation oncology rotations into the residency program curriculum.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".