Trends in Radiology Fellowship Training: A Canadian Review 2009-2011
Bibliographic record
Abstract
PURPOSE: To assess the percentage, type, and location of radiology fellowships chosen by graduating Canadian residents between 2009 and 2011. METHODS: A short e-mail questionnaire was sent to the radiology program directors at all 16 institutions in Canada that provide English or French residency. The responses were collected between December 6, 2010, and May 20, 2011. RESULTS: A 75% response rate was observed for the survey: 76%-79% residents were enrolled in radiology fellowship training. In 2009-2010, 72%-73% of residents remained in Canada. This dropped to 51% in 2011. In 2009-2010, 22%-23% of residents chose U.S.-based radiology training. This rose to 49% in 2011. Europe was chosen by 0%-4% of residents: all of whom were French-speaking residents, and all programs were in France. Relatively consistent percentages of radiology residents choose abdominal (19%-30%), cardiac (4%-7%), musculoskeletal (12%-20%), and pediatrics (2%-5%) from year to year. Greater variability was noted in chest (2%-9%), women's imaging (0%-14%), intervention radiology (6%-18%), and neuroradiology (2%-18%). Radiology fellowships in split subspecialties, which were available at a small number of institutions, were chosen by 8%-9% of the residents. CONCLUSIONS: Nearly 4 of 5 residents choose radiology fellowship training. In 2011, there was a 2-fold increase in the number of residents who chose training in the United States. This may be a 1-year outlier but should be observed. A wide range of fellowships were chosen, with consistent numbers in some core fellowships and variability in others year to year. Limited exploration of the rationale for, or employability value of, radiology fellowship choices has been done in Canada. Nearly 1 of 10 residents chose split radiology fellowships, an option limited by availability at few centers. The value of expanding this option is worthy of investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".