Spectrum of care toolkit: identifying and communicating evidence-based options
Bibliographic record
Abstract
Objective: Using a case-based example, to provide a video tutorial on the use of a framework to identify, review, and communicate to a pet owner (veterinary client) evidence-based findings that enable the provision of spectrum-of-care options for patient management, demonstrate use of the Value Matrix, and inform shared decision-making. Animals: Any veterinary patient for which evidence-based care options along the continuum of acceptable care (often referred to as spectrum of care) is sought. Methods: Evidence-based veterinary medicine involves identifying a relevant clinical question facing the patient, client, and veterinarian and acquiring and appraising the evidence, informing the options applied to the unique case. An easily followed framework informs this process to maximize success in identifying existing veterinary evidence. Several tools, including the Value Matrix, allow for effective client communication of the options for the patient, highlighting the advantages and disadvantages for each option for situation-specific criteria. Results: Evidence-based options are identified and communicated easily, even for complex clinical questions for which there is minimal published existing evidence. Clinical Relevance: This tutorial provides practical tools that can assist veterinary professionals in collaborating with clients on making evidence-based decisions, are integral to practicing broadly across the spectrum of care, and can be adapted to each unique pet, pet owner, geography, and veterinary clinic scenario.
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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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".