Online Instructional Module Improves Student Ability to Evaluate for Radiographic Small Intestinal Obstruction in Dogs
Bibliographic record
Abstract
Radiographic diagnosis of mechanical small intestinal obstruction in dogs is a common and important skill for the small animal practitioner; however, developing skills in this area is challenging. Feedback and practice are both essential to increasing accuracy in interpretation, which can be maximized with focused, self-paced training. We aimed to characterize the efficacy of a self-paced, online module in improving student skill and confidence in interpretation of radiographic small intestinal obstruction in dogs. An instructional module was developed using a series of abdominal radiographs of dogs with and without small intestinal mechanical obstruction; the module allows students to form a diagnosis and then provides feedback on each case. Before and after using the module, 32 veterinary students completed a survey and 20-case abdominal radiograph quiz. This quiz presented 20 abdominal radiograph cases and asked if each radiographic series demonstrated small intestinal mechanical obstruction and the student's confidence level in the diagnosis. Prior to completing the module, average quiz accuracy was 64%; this accuracy increased to 83% after completing the module. Student confidence in their diagnosis also increased post-module completion. On surveys, students had a low initial confidence in their ability to accurately interpret normal versus small intestinal obstruction via abdominal radiographs; confidence improved on the post-module survey. Students made positive comments regarding the module and reviewed it positively as a learning experience. This instructional module appears to be a successful way to teach and reinforce radiographic interpretation skills for small intestinal obstruction in dogs.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".