A retrospective study on findings of canine hip dysplasia screening in Kenya
Bibliographic record
Abstract
Aim: \n The current study was undertaken to evaluate the findings of canine hip dysplasia screening in Kenya. \nMaterials and Methods: \n Records for 591 dogs were included in this study. The data was obtained from the national \nscreening office, Kenya Veterinary Board, for the period between the years 1998 and 2014. Monthly screening records were \nassessed and information relating to year of evaluation, breed, sex, age, and hip score captured. Descriptive statistics of hip \nscores was computed based on year, sex, age, and breed. \nResults: \nA \ntotal of 591 records from the year 1998 to 2014 were retrieved at the National Screening Centre, the Kenya \nVeterinary Board. Each record was examined and data pertaining to year of screening, the breed, sex, age of the dogs, \nand the total hip score were recorded. The highest number of dogs screened for hip dysplasia (HD) was in the year 2009 \nand the lowest in the year 1998. More females than males were screened for HD and the mean age of all the dogs was \n22.9±12.7 months. The most common breeds of dogs screened during the study period were German Shepherd (67.0%), \nRottweiler (15.6%), and Labrador Retriever (12.2%). The mean hip score for the 591 dogs was 15.1±10.9 and the median \n12.0. The mean hip scores per breed were; German Shepherd (16.3±12.1); Golden Retriever (16.0); Hungarian Vizla (15.0); \nLabrador Retriever (3.0±6.7); Great Dane (13.3±3.2); Rottweiler (12.2±8.2); Doberman (10.3±4.2); Rhodesian Ridgeback \n(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 \nHD in 32.7%, Borderline HD in 37.7%, fair HD in 6.9%, and good HD in 6.1%. \nConclusion: \n Canine HD is a common occurrence in Kenya with most dogs suffering mild to border line HD. In addition, \nGerman Shepherd and Golden Retriever appear to be the most affected breeds. It is therefore recommended that stringent \nmeasures be imposed to dog breeding programs to avoid transmission of this undesirable trait and consequently improve the \nwelfare and the quality of dog breeds in Kenya.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".