Laparoscopy and thoracoscopy in small animal surgery: A 2020 survey of small animal diplomate and resident veterinary surgeons
Bibliographic record
Abstract
OBJECTIVE: To document the utilization and training of laparoscopic and thoracoscopic minimally invasive surgery (MIS) techniques within the American, European, Australian and New Zealand Colleges of Small Animal Veterinary Surgeons (ACVS, ECVS, and ANZCVS) in 2020. STUDY DESIGN: Observational study. SAMPLE POPULATION: Diplomates and residents of the ACVS, ECVS, and FANZCVS. METHODS: An electronic survey was sent using veterinary list servers. Questions were organized into categories evaluating (1) the demographics of the study population and the caseload, (2) comfort level with specific procedures, (3) motivating factors and limitations, and (4) surgical training and the role of the governing bodies. RESULTS: Respondents included 111 practicing surgeons and 28 residents. Respondents' soft-tissue MIS caseloads had increased since they first started performing MIS; however, most respondents were only comfortable performing basic laparoscopy. Over half of the respondents agreed on the patient benefits and high standard of care provided by MIS. Perceived adequate soft-tissue training in MIS during residency was strongly associated with perceived proficiency at the time of survey response. Most respondents agreed that the specialty colleges should take a more active role in developing standards for soft-tissue MIS, with residents agreeing that a required standardized course would be beneficial. CONCLUSION: Soft-tissue MIS is widely performed by diplomates and residents. Perceived adequate soft-tissue MIS training was strongly associated with perceived proficiency. CLINICAL SIGNIFICANCE: There is substantial underutilization of advanced MIS techniques in veterinary specialty surgical practice, which might be improved by a stronger focus on MIS training during residency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".