Twitter in the Veterinary Diagnostic Imaging Classroom: Examination Outcomes and Student Views
Bibliographic record
Abstract
Radiographic lesion identification and differential diagnosis list generation can be difficult for veterinary students; thus, a novel means of distributing cases for study could improve students' engagement and learning. The goal of this study was to determine whether using Twitter as an adjunct means of studying diagnostic imaging would improve student outcomes on the final exam for a radiology course. A secondary goal was to determine students' preferred means of accessing additional cases for study. Twitter was used in a third-year veterinary radiology course to provide additional optional radiographic cases that were relevant to the topics covered in the course. At the end of the semester, students completed a survey to report their prior and current use of Twitter and to give preferences as to further distribution of optional cases. Mean final examination scores were compared between students who used Twitter in their studies and those who did not. No significant difference was found between the mean final examination score for each group (22.2; p = .98). Only 3% of respondents ( n = 2/79) preferred Twitter as a means of receiving additional radiographic cases; Moodle (the Web platform for classwork used at this institution) and Facebook were the most preferred platforms for further cases, receiving 41% ( n = 32/79) and 23% ( n = 18/79) of votes, respectively. Educational use of Twitter did not improve student examination performance in diagnostic imaging, and other media platforms may be more beneficial than Twitter for encouraging student use of additional resources.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".