Audiovisual Content for a Radiology Fellowship Selection Process During the COVID-19 Pandemic: Pilot Web-Based Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Traditional radiology fellowships are usually 1- or 2-year clinical training programs in a specific area after completion of a 4-year residency program. OBJECTIVE: This study aimed to investigate the experience of fellowship applicants in answering radiology questions in an audiovisual format using their own smartphones after answering radiology questions in a traditional printed text format as part of the application process during the COVID-19 pandemic. We hypothesized that fellowship applicants would find that recorded audiovisual radiology content adds value to the conventional selection process, may increase engagement by using their own smartphone device, and facilitate the understanding of imaging findings of radiology-based questions, while maintaining social distancing. METHODS: One senior staff radiologist of each subspecialty prepared 4 audiovisual radiology questions for each subspecialty. We conducted a survey using web-based questionnaires for 123 fellowship applications for musculoskeletal (n=39), internal medicine (n=61), and neuroradiology (n=23) programs to evaluate the experience of using audiovisual radiology content as a substitute for the conventional text evaluation. RESULTS: Most of the applicants (n=122, 99%) answered positively (with responses of "agree" or "strongly agree") that images in digital forms are of superior quality to those printed on paper. In total, 101 (82%) applicants agreed with the statement that the presentation of cases in audiovisual format facilitates the understanding of the findings. Furthermore, 81 (65%) candidates agreed or strongly agreed that answering digital forms is more practical than conventional paper forms. CONCLUSIONS: The use of audiovisual content as part of the selection process for radiology fellowships is a new approach to evaluate the potential to enhance the applicant's experience during this process. This technology also allows for the evaluation of candidates without the need for in-person interaction. Further studies could streamline these methods to minimize work redundancy with traditional text assessments or even evaluate the acceptance of using only audiovisual content on smartphones.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".