A retrospective study on findings of canine hip dysplasia screening in Kenya
Bibliographic record
Abstract
AIM: The current study was undertaken to evaluate the findings of canine hip dysplasia screening in Kenya. MATERIALS AND METHODS: Records for 591 dogs were included in this study. The data was obtained from the national screening office, Kenya Veterinary Board, for the period between the years 1998 and 2014. Monthly screening records were assessed and information relating to year of evaluation, breed, sex, age, and hip score captured. Descriptive statistics of hip scores was computed based on year, sex, age, and breed. RESULTS: A total of 591 records from the year 1998 to 2014 were retrieved at the National Screening Centre, the Kenya Veterinary Board. Each record was examined and data pertaining to year of screening, the breed, sex, age of the dogs, and the total hip score were recorded. The highest number of dogs screened for hip dysplasia (HD) was in the year 2009 and the lowest in the year 1998. More females than males were screened for HD and the mean age of all the dogs was 22.9±12.7 months. The most common breeds of dogs screened during the study period were German Shepherd (67.0%), Rottweiler (15.6%), and Labrador Retriever (12.2%). The mean hip score for the 591 dogs was 15.1±10.9 and the median 12.0. The mean hip scores per breed were; German Shepherd (16.3±12.1); Golden Retriever (16.0); Hungarian Vizla (15.0); Labrador Retriever (3.0±6.7); Great Dane (13.3±3.2); Rottweiler (12.2±8.2); Doberman (10.3±4.2); Rhodesian Ridgeback (9.6±3.8); and Boxer (9.3±0.6). Based on the hip score, moderate to severe HD was diagnosed in 16.6% of the dogs, mild HD in 32.7%, Borderline HD in 37.7%, fair HD in 6.9%, and good HD in 6.1%. CONCLUSION: Canine HD is a common occurrence in Kenya with most dogs suffering mild to border line HD. In addition, German Shepherd and Golden Retriever appear to be the most affected breeds. It is therefore recommended that stringent measures be imposed to dog breeding programs to avoid transmission of this undesirable trait and consequently improve the welfare and the quality of dog breeds in Kenya.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".