Comparing Two Resources Used to Teach Pulmonary Patterns for a Flipped Veterinary Radiology Course
Bibliographic record
Abstract
The flipped classroom has been gaining momentum within medical education circles. Pre-class assignments are an important component of this pedagogical approach. In this study, a section of the introductory course to veterinary medical imaging was taught using a flipped classroom, and the effectiveness of two different pre-classroom assignments was evaluated. The pre-classroom assignments consisted of either short videos or readings. Both had similar content, which included basic information about pulmonary patterns of disease on chest radiographs. Learning outcomes were assessed by in-classroom and final examination questions. Student learning self-assessments and student satisfaction were also evaluated via an online survey. Students in the video group answered more of the in-classroom questions correctly (71% video vs. 63% reading group; p = .01) and had higher scores on the final examination (83% video vs. 75% reading group; p = .02). There was also a higher student satisfaction with the videos versus the reading assignment. However, we found no significant difference in the student self-assessments of learning or participation in class. An additional finding of this study related to the ongoing difficulties students were having with the learning objectives, including differentiating a pathological process from a normal, or normal variant, recognizing the different pulmonary patterns, and developing a differential diagnoses list, despite the pre-classroom assignments and large group learning sessions. This speaks to the difficulty in developing confidence in pulmonary pattern recognition on chest radiographs, a skill that requires considerable training and time investment.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 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".