Survey of the Large-Animal Diplomates of the American College of Veterinary Internal Medicine Regarding Knowledge and Clinical Use of Polymerase Chain Reaction: Implications for Veterinary Education
Bibliographic record
Abstract
A questionnaire was developed to document the knowledge base of large-animal diplomates of the American College of Veterinary Internal Medicine (ACVIM) regarding polymerase chain reaction (PCR) technology and to identify the common use of this technology in equine practice. Ninety-three of the 278 mailed questionnaires were returned, for an overall response rate of 33.4%. Ninety respondents (99%) reported being familiar with the general principles of nucleic acid probe technology; however, only 52 (57%) knew the difference between conventional (traditional) and real-time (second-generation) PCR. The majority of the respondents (88%) emphasized the need for continuing education on molecular diagnostics. Eighty-four (92%) of the respondents regularly use PCR (conventional and/or real-time) for the detection of equine pathogens, and 80 (88%) commonly submit their samples to university/state veterinary laboratories. Blood, nasal swabs, and feces are the three equine specimens most commonly submitted for PCR analysis of Streptococcus equi, Lawsonia intracellularis, Neorickettsia risticii, equine herpesvirus 1/4, Rhodococcus equi, Sarcocystis neurona, and equine influenza virus. Diplomates reported costs associated with molecular diagnostics and unreliability of PCR as the most common limitations of PCR. Didactic training in veterinary curricula and during continuing-education opportunities continues to be necessary to produce veterinarians who have an understanding of the clinical applications of molecular diagnostics.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".