Comparing Two Resources Used to Teach Pulmonary Patterns for a Flipped Veterinary Radiology Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it