Development and Validation of a Simulation Model for Collection of Canine Vaginal Samples
Bibliographic record
Abstract
Vaginal cytology is a widely used cytological technique mostly taught by observation, either through direct tutoring or videos. To the best of our knowledge, vaginal cytology simulators have never been assessed in veterinary medicine. Twenty-five undergraduate students with no prior experience in canine vaginal sampling were randomly assigned to two groups that practiced the procedure in either a simulator or a live animal. An inverted classroom design was followed. After observing a video tutorial, students practiced with the simulator/live animal for two classes. Three weeks later, they performed a vaginal cytology on a live animal being recorded. The videos were evaluated through an objective structured clinical examination (OSCE) by an observer blinded to the student's groups. The learning outcome was compared through OSCE pass rates and questionnaires. The simulation model was made by 3D printing and soft silicone for the vulvar labia, having pink and blue colored vaseline in the correct and incorrect locations for sampling. The model was economic and accurately replicated the female reproductive tract. It provided immediate feedback to students, who obtained pink or blue swabs from the correct and incorrect locations, respectively. Students reported that three to five or more attempts were needed to properly learn the procedure, thus justifying the need for a simulator. No differences in the OSCE pass rates were observed between the groups. The simulation model was effective for learning the vaginal cytology procedure, replacing the use of live animals. This low-cost model should be incorporated in the tool-kit of reproduction classes. This translation was provided by the authors. To view the full translated article visit: https://doi.org/10.3138/jvme-2022-0141.pt
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".