Rejections in an non-purpose bred assistance dog population: Reasons, consequences and methods for screening
Bibliographic record
Abstract
Assistance dogs aid people with various impairments on a daily basis. To become an assistance dog, a strict selection procedure and intensive training period must be successfully completed. Consequently, not every dog acquired for this purpose, becomes an assistance dog. The purpose of this study was to investigate reasons for failure and the financial consequences thereof for assistance dog associations that do not have a dedicated breeding program for their dogs. Data were collected for a total of 537 dogs enlisted between 2001 and 2015 and purchased out of the general dog population by five Belgian assistance dog associations. Only 60 percent of the dogs actually became an assistance dog and the main reasons for failure were related to undesirable behavioural characteristics and orthopaedic disorders. The estimated average financial loss per rejected dog was found to be 10524 euro. A detailed comparison of the two most popular breeds (Golden Retriever and Labrador Retriever) within the guide dogs and mobility assistance dogs revealed no significant difference in probability of successfully completing the training. However, a comparison of orthopaedic screening methods revealed a higher rejection with computed tomography for elbow dysplasia and laxity-based radiographical techniques for hip dysplasia compared to radiography and the standard ventrodorsal hip extend radiograph alone, respectively. Based on these results, we provide several suggestions to increase the probability of success.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".