Multidisciplinary approach to diagnostic radiology education: a novel educational intervention for Turkish medical students
Bibliographic record
Abstract
Teleconferencing can facilitate a multidisciplinary approach to teaching radiology to medical students.This study aimed to determine whether an online learning approach enables students to appreciate the interrelated roles of radiology and other specialties during the management of different medical cases.Turkish medical students attended five 60-90-minute online lectures delivered by radiologists and other specialists from the United States and Canada through Zoom meetings between November 2020 and January 2021.Student ambassadors from their respective Turkish medical schools recruited their classmates with guidance from the course director.Students took a pretest and posttest to assess the knowledge imparted from each session and a final course survey to assess their confidence in radiology and the value of the course.A paired t-test was used to assess pretest and posttest score differences.A 4-point Likert-type scale was used to assess confidence rating differences before and after attending the course sessions.A total of 1,458 Turkish medical students registered for the course.An average of 437 completed both pre-and posttests when accounting for all five sessions.Posttest scores were significantly higher than pretest scores for each session (P < 0.001).A total of 546 medical students completed the final course survey evaluation.Students' rating of their confidence in their radiology knowledge increased after taking the course (P < 0.001).Students who took our course gained an appreciation for the interrelated roles of different specialties in approaching medical diagnoses and interpreting radiological findings.These students also reported an increased confidence in radiology topics and rated the course highly relevant and insightful.Overall, our findings indicated that multidisciplinary online education can be feasibly implemented for medical students by video teleconferencing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".