2025 AAHA Referral Guidelines
Bibliographic record
Abstract
Across the many types of specialty practitioners and hospitals, the requirements for veterinary patient referrals vary from one-time consultations to long-term case oversight and management. These guidelines propose a structured and technology-based approach to optimize the referral process for patients, clients, and veterinary teams. They emphasize a family-centered health care approach that keeps the focus on patients and clients through consistent collaboration between primary and specialty care teams. Collaboration between primary care teams and specialty care teams requires detailed and timely communication and medical records sharing. Veterinary clients also need content-rich and supportive conversations as they navigate often stressful clinical situations with their pets, including the realities of referral care costs, prognoses, and possible ongoing treatments and/or management of chronic conditions. These guidelines establish the concepts, roles, client communication strategies, and timelines that will promote successful referral relationships. Later sections offer detailed insights into the key responsibilities for the primary and specialty care team, from the initial contact before referral, through the referral itself, and then back to primary care team oversight. The final sections consider strategies to increase access to care using team optimization and telehealth, as well as possible obstacles in the referral process and how to address or avoid them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.086 | 0.045 |
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".