Evaluating the Outcomes and Trainee Performance of a Canadian Medical Imaging Clinician Investigator Program
Bibliographic record
Abstract
Purpose: To measure the research productivity of trainees from the University of Toronto’s Medical Imaging Clinician Investigator Program (MI-CIP) and comparing it with the research productivity of trainees from MI-non-CIP and General Surgery (GSx) Clinician Investigator Program. Methods: We identified residents who completed an MI-CIP, MI-non-CIP and GSx-CIP from 2006-2016. In each group of trainees, we assessed 3 research productivity outcomes with non-parametric tests before residency and at 7 years post-CIP completion/post-graduation. Research productivity outcomes include the number of total publications, the number of first-author publications, and the publication’s average journal impact factor (IF). Results: We identified 11 MI-CIP trainees (male/female: 9 [82%]/2 [18%]), 74 MI-non-CIP trainees (46 [62%]/28 [38%]) and 41 GSx-CIP trainees (23 [56%]/18 [44%]). MI-CIP trainees had statistically significant higher research productivity than MI-non-CIP in all measured outcomes. The median (interquartile range, IQR) number of total publications of MI-CIP vs MI-non-CIP trainees was 5.0 (8.0) vs 1.0 (2.0) before residency and 6.0 (10.0) vs .0 (2.0) at 7 years post-CIP completion/post-graduation. The median (IQR) first-author publications of MI-CIP vs MI-non-CIP trainees was 2.0 (3.0) vs .0 (1.0) before residency and 2.0 (4.0) vs (.0) (1.0) at 7 years post-CIP completion/post-graduation. The median (IQR) average journal IF of MI-CIP vs MI-non-CIP trainees was 3.2 (2.0) vs .3 (2.4) before residency and 3.9 (3.2) vs .0 (2.6) at 7 years post-CIP completion/post-graduation. Between MI-CIP and GSx-CIP trainees, there were no significant differences in research productivity in all measured outcomes. Conclusion: MI-CIP trainees actively conducted research after graduation. These trainees demonstrated early research engagement before residency. The similar research productivity of MI-CIP vs GSx-CIP trainees shows initial success of MI-CIP trainees.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".