Implementation of a Low-Cost 3D-Printed Feline Larynx Model for Veterinary Students
Bibliographic record
Abstract
Endotracheal intubation (EI) in domestic cats is an important skill that veterinary students learn in order to perform anesthesia safely in this species. Implementing a 3D-printed larynx model (LaryngoCUBE) during the instruction process may improve student’s learning of EI in felines. Twenty-two third-year students performed EI in cats with standard training (ST), and 16 students trained with the model (MT) the day before the laboratory. It was evaluated whether training with the model decreases the time and number of EI attempts, students’ perceived difficulty performing EI using a visual analog score (VAS; 0 cm = very easy, 10 cm = extremely difficult; median [minimum–maximum]), and the incidence of failure to perform EI. The EI time on ST (58 [18–160] seconds) was longer, but not statistically different from MT (29 [13–120] seconds; p = .101). The number of EI attempts on ST (2 [1–3]) was higher than MT (1 [1–3]; p = .005). The VAS on the ST and MT were 4.5 (0.0–10.0) cm and 3.0 (0.2–10.0) cm, respectively ( p = .029). The failure rate was 27% on the ST and 25% on the MT ( p = 1.000). Students who practiced with a larynx model took fewer attempts to perform EI, tended to be faster, and found that EI was easier. However, the EI success rate in MT was not improved.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".