Strategies to Improve Case Outcome When Referral Is Not Affordable
Bibliographic record
Abstract
We conducted a survey-based study to determine whether on-site consultations and cost-effective protocols are beneficial to general practitioners handling challenging small animal internal medicine patients when owners cannot afford referral and whether fourth-year veterinary students benefit from training in this area. Fifteen general practices were visited over 12 months by a board-certified internist and students. On-site consultations for patients belonging to owners who could not afford referral were conducted by the internist. Students and general practitioners completed pre- and post-participation surveys. Students' surveys contained questions about comfort level with complicated cases on a budget and knowledge gained from, and perception of, the on-site consultations and protocol development. Practitioners' surveys contained questions about comfort level and experience with complicated internal medicine cases, the benefit of the consultations, and the cost-effective protocols, which were compiled into a booklet for practitioners. All students and practices completed the pre-survey, and 56 of 60 (93.3%) of the students and 13 of 15 (86.7%) of the practices completed the post-survey. Approximately 68% of students believed their comfort level with budget-limited cases improved and that they benefited from participation in the consultations and protocol development. Similarly, most general practitioners believed these strategies were highly beneficial. The cost of veterinary care, especially referral medicine, is unaffordable for many owners. Veterinary students should be exposed to these challenges and trained in cost-effective approaches. Similarly, general practitioners may be able to more successfully and efficiently diagnose and treat challenging internal medicine cases using the proposed strategies when owners decline referral.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".