What are the Clinical Questions of Practicing Veterinarians?
Bibliographic record
Abstract
Clinical questions are central to learning among veterinarians and drive informal learning during clinical practice. We set out to classify the clinical questions of practicing veterinarians using a taxonomy previously validated in human medicine. This prospective observational study used a convenience sample of 12 veterinarians in private, small-animal practices. We used three methods to gather clinical questions from the veterinarians: direct observation (asking veterinarians after each encounter), self-report via e-mail, and self-report via data-collection pocket cards. We then classified these questions using a validated taxonomy of question types, as well as by clinical category. A total of 157 clinical questions were collected; 99 were about dogs, 33 were about cats, and 25 were about multiple species or did not specify a species. Nearly half of the questions were rated as high priority, and only 11.5% as low priority. Over half of the questions (53%) were about treatment and 20% were about diagnosis. The two most common question types were "Is drug X indicated in situation Y or for condition Y?" and "How should I treat finding/condition Y (given situation Z)?" Overall, 5 of 57 question-type categories accounted for over half of the questions. The most common clinical categories were pharmacology, endocrine, musculoskeletal, and general surgery. This is the first study to systematically identify and classify the clinical questions of veterinarians. A better understanding of these questions can be used to inform the development of continuing-education (CE) activities that are directly responsive to the information needs of participants.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".