Connecting Knowledge Resources to the Veterinary Electronic Health Record: Opportunities for Learning at Point of Care
Bibliographic record
Abstract
Electronic health records (EHRs) provide clinical learning opportunities through quick and contextual linkage of patient signalment, symptom, and diagnosis data with knowledge resources covering tests, drugs, conditions, procedures, and client instructions. This paper introduces the EHR standards for linkage and the partners-practitioners, content publishers, and software developers-necessary to leverage this possibility in veterinary medicine. The efforts of the American Animal Hospital Association (AAHA) Electronic Health Records Task Force to partner with veterinary practice management systems to improve the use of controlled vocabulary is a first step in the development of standards for sharing knowledge at the point of care. The Veterinary Medical Libraries Section (VMLS) of the Medical Library Association's Task Force on Connecting the Veterinary Health Record to Information Resources compiled a list of resources of potential use at point of care. Resource details were drawn from product Web sites and organized by a metric used to evaluate medical point-of-care resources. Additional information was gathered from questions sent by e-mail and follow-up interviews with two practitioners, a hospital network, two software developers, and three publishers. Veterinarians with electronic records use a variety of information resources that are not linked to their software. Systems lack the infrastructure to use the Infobutton standard that has been gaining popularity in human EHRs. While some veterinary knowledge resources are digital, publisher sites and responses do not indicate a Web-based linkage of veterinary resources with EHRs. In order to facilitate lifelong learning and evidence-based practice, veterinarians and educators of future practitioners must demonstrate to veterinary practice software developers and publishers a clinically-based need to connect knowledge resources to veterinary EHRs.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".