Canine Ovariohysterectomy: A Survey of Surgeon Concerns and Surgical Complications Encountered by Newly Graduated Veterinarians
Bibliographic record
Abstract
The objective of this study was to document newly qualified veterinarians' concerns and surgical complications encountered during canine ovariohysterectomy (cOVH) during the first year of general practice. A questionnaire investigating concerns about cOVH procedures was sent to all final-year veterinary students (group 1) enrolled at five UK universities. Participants were later asked to complete a similar questionnaire 6 months (group 2) and 12 months (group 3) after graduation, which involved grading their concern about different aspects of the cOVH procedure and reporting surgical complications encountered after completing three cOVHs. Responses were compared between different time points. There were 196 respondents in group 1, 55 in group 2, and 36 in group 3. Between groups 1 and 2, there was a statistically significant reduction in the respondents' levels of concern in every aspect of cOVH (p<.05). Between groups 2 and 3, there was no statistically significant change in respondents' levels of concern in any aspect of cOVH (p≥.21). There was a significant reduction in the number of complications encountered by veterinarians in group 3 (39/102, 38.2%) compared to those in group 2 (117/206, 56.8%) (p=.002). Employers should anticipate high levels of concern regarding all aspects of cOVHs in new graduates, and supervision during the first 6 months may be particularly useful.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".