Bibliographic record
Abstract
Training students to perform emergency procedures is a critical but challenging component of veterinary education. Thoracocentesis is traditionally taught in the classroom, with students progressing to "see one, do one, teach one" during the clinical phase of their education. This method of teaching does not permit students to gain proficiency before performing thoracocentesis on a live animal in a high-stakes, high-stress environment and is dependent on the availability of animals requiring the procedure. A veterinary thoracocentesis simulator has been created to allow students an opportunity for repetitive practice in a low-stakes environment. This study evaluated the face, content, and construct validity of the thoracocentesis simulator. Face and content validation were confirmed by survey results, and construct validity was assessed through comparison of student and veterinarian performance on the simulator. Students' median checklist and global rating scores were significantly lower than those of the veterinarians, and students took significantly longer to perform the procedure, indicating that the simulator was able to differentiate the relative expertise of the user and establishing construct validity. This study supported the use of the thoracocentesis simulator for educators to demonstrate proper technique, for students to practice the steps needed to perform the procedure and experience an approximation of the tactile aspects of the task, and for formative assessment before performing the procedure on client-owned animals.
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.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".