Factors influencing veterinarian referral to oncology specialists for treatment of dogs with lymphoma and osteosarcoma in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To elucidate factors influencing practitioner decisions to refer dogs with cancer to veterinary oncology specialists. DESIGN: Cross-sectional study. SAMPLE: 2,724 Ontario primary care companion animal veterinarians. PROCEDURES: Practitioners were invited to participate in a survey involving clinical scenarios of canine cancer patients, offered online and in paper format from October 2010 through January 2011. Analyses identified factors associated with the decision to refer patients to veterinary oncology specialists. RESULTS: 1,071 (39.3%) veterinarians responded, of which 603 (56.3%) recommended referral for dogs with multicentric lymphoma and appendicular osteosarcoma. Most (893/1,059 [84.3%]) practiced within < 2 hours' drive of a specialty referral center, and most (981/1,047 [93.7%]) were completely confident in the oncology service. Few (230/1,056 [21.8%] to 349/1,056 [33.0%]) were experienced with use of chemotherapeutics, whereas more (627/1,051 [59.7%]) were experienced with amputation. Referral was associated with practitioner perception of patient health status (OR, 1.54; 95% confidence interval [CI], 1.15 to 2.07), the interaction between the client's bond with the dog and the client's financial status, practitioner experience with treating cancer (OR, 2.79; 95% CI, 1.63 to 4.77), how worthwhile practitioners considered treatment to be (OR, 1.66 to 3.09; 95% CI, 1.08 to 4.72), and confidence in the referral center (OR, 2.20; 95% CI, 1. 11 to 4.34). CONCLUSIONS AND CLINICAL RELEVANCE: Several factors influenced practitioner decisions to refer dogs with lymphoma or osteosarcoma for specialty care. Understanding factors that influence these decisions may enable practitioners to appraise their referral decisions and ensure they act in the best interests of patients, clients, and the veterinary profession.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".