Bibliographic record
Abstract
Veterinary teaching hospitals (VTHs) have traditionally obtained most of their patient and client base through the referral process. This worked well until the recent explosive growth of specialty practices, which compete not only for patients but also for faculty and graduating residents. Veterinary schools have had to meet this challenge by increasing both efficiency and the quality of services provided to referring veterinarians. Practitioners refer mainly because of discomfort with a case and the belief that clients will get better treatment at a referral hospital than they themselves can provide. Practitioners choose not to refer because of geography, perceived cost, or lack of confidence in the services offered. Referring veterinarians expect regular communication about services offered, access to receiving clinicians for consultation, convenient scheduling, and efficient communication and follow-up from the receiving veterinarian. They also expect the relationship between them and their clients to be maintained and enhanced. Receiving veterinarians expect a summary letter and copies of all relevant records, including radiographs. They also expect the client to have been informed about the approximate costs of referral. VTHs can develop better relationships with referring veterinarians through education, newsletters, referral guides, practice visits, and Web sites. Inadequate communication and lack of involvement on the part of the referring veterinarian are the major impediments to efficient referrals and practice growth.
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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".