Implementation of a Fine Needle Aspirate Simulation Model
Bibliographic record
Abstract
Being able to appropriately perform fine needle aspiration (FNA) collecting techniques and sample preparation is essential in obtaining a diagnostic sample, which is a critical skill for veterinary practitioners. Collection and preparation of cytologic samples are skills gained through practice. Experience leads to refinement of technique and improved diagnostic quality. Using live patients for mass skills training is not feasible; therefore, an aspiration simulation model and laboratory session was developed to reinforce physical exam skills, appropriate selection of sample collection supplies, and collection technique. Materials for the models include Ping-Pong balls, silicone, instant vanilla pudding mix, water, and stuffed animals. The laboratory session allows veterinary students to practice lesion identification, isolation, aspiration, and successful preparation. Subsequent submission of the collected sample involves being able to expel and spread the sample on a slide and proper labeling. While the simulation experience was initially developed for a short course with 12 students, it has recently been incorporated into the required clinical pathology clinical year rotation for up to 100 fourth-year veterinary students. The model is inexpensive and efficient and allows for technique development and immediate instructor assessment and feedback.
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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".