Mistakes, Misrepresentation, and Misunderstanding on Applications to a Canadian Diagnostic Radiology Residency Program
Bibliographic record
Abstract
Purpose: Determine the educational background, research publications/presentations experience, and rates of research publication and presentation inaccuracies in applications to a Canadian diagnostic imaging residency program. Method: The education and publication/presentation sections of the Canadian Resident Matching Service form for all applicants to the University of Saskatchewan diagnostic imaging residency program from 2019–20 and 2020–21 were reviewed. Number of advanced degrees (Master’s/PhDs), publications, and presentations were recorded. Accuracy of publications listed was confirmed via PubMed-MEDLINE, journal’s website, or internet searching. Accuracy of presentations was confirmed via society and residency program websites. Inaccuracies of non-authorship, incorrect authorship order/status (self-promotion or demotion), and nonexistence of article/presentation from a verifiable source were recorded. Result: There were a total of 106 applicants. Thirty (28%) had advanced degrees. There were 230 publications from 61 applicants with inaccuracies in only 5 (2%) of the publications (2 self-promotion, 3 self-demotion). For the 77 publications listed as pending, 25 (31%) were published within 6 months of applications deadlines with 1 non-authorship, 1 self-promotion, and 1 self-demotion. For scientific presentations, there were 467 listed presentations by 91 applicants. Two hundred and twenty-one presentations were from verifiable sources with inaccuracies in 28 (13%) of presentations (9 self-promotion, 9 self-demotion, 1 non-authorship, and 9 non-existence). Conclusion: Despite some uncertainty with scientific articles reported as pending and scientific presentations, radiology residency applicants are accurately representing their published articles with a negligible number of misrepresentations. Canadian radiology residency programs should regard the publication profiles of the applicants with a high level of confidence.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".