Abnormal bone mineralization in a puppy fed an imbalanced raw meat homemade diet diagnosed and monitored using dual‐energy X‐ray absorptiometry
Bibliographic record
Abstract
A 4-month-old male Old English Sheepdog was presented for evaluation of a raw meat-based homemade diet after a 1-month history of progressive lameness. Marked dietary deficiencies were detected, which included calcium, phosphorus and vitamin D. Hypovitaminosis D and hypocalcaemia were diagnosed by serum analysis. Evidence of severe diffuse osteopenia was noted on survey radiographs. Dual-energy X-ray absorptiometry (DEXA) was used to quantify bone mineral content and density and compare to published reference ranges. The puppy's initial bone mineralization was markedly subnormal, with bone mineral density 66% lower than expected, and bone mineral content 40% lower than expected. Subsequent DEXA scans were performed at intervals during the puppy's recovery to document the rate of bone re-mineralization and guide therapeutic recommendations. Marked improvement was achieved within 4 months through exercise control and feeding of a diet appropriately formulated for large breed puppy growth and development. This report reinforces the necessity of thorough dietary history and highlights the potential for malnutrition in pets fed homemade and raw meat-based diets. Use of DEXA has rarely been reported in clinical cases, yet can be a valuable tool for diagnosing and monitoring cases with abnormal bone mineralization. Further studies using DEXA to track bone mineralization in healthy puppies are encouraged to develop a more robust reference range of bone mineralization in growing dogs of varying sizes, weights and ages.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".