Twenty-five components of a baseline, best-practice companion animal physical exam established by a panel of experts
Bibliographic record
Abstract
OBJECTIVE: To establish the components of a best-practice, baseline companion animal physical exam (CAPE). SAMPLE: 25 small animal veterinary internists and 20 small animal primary care veterinarians, all teaching the CAPE at veterinary colleges in the US, Canada, and Australia. PROCEDURES: Using the Delphi Method of Consensus, 3 rounds of online questionnaires were sent to participants. The first round included demographic questions, questions about teaching the physical exam, and an open-ended question allowing participants to record details of how they conduct a CAPE. In the second round, participants were asked to rate components of the CAPE, which were derived from round 1, as "always examine," "only examine as needed," or "undecided." Following round 2, any component not reaching 90% consensus (set a priori) for the response "always examine" was put forth in round 3, with a summary of comments from the round 2 participants for each remaining component. RESULTS: 35 components of a baseline CAPE were identified from round 1. The 25 components that reached 90% consensus by the end of round 3 were checking the oral cavity, nose, eyes, ears, heart, pulse rate, pulse quality, pulse synchrony, lungs, respiratory rate, lymph nodes, abdomen, weight, body condition score, mucous membranes, capillary refill time, general assessment, masses, haircoat, skin, hydration, penis and testicles or vulva, neck, limbs, and, in cats only, thyroid glands. CLINICAL RELEVANCE: The findings establish an expert panel's consensus on 25 components of a baseline, best-practice CAPE that can be used to help inform veterinary curricula, future research, and the practice of veterinarians.
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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 | 0.031 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".