NEUROLOGICAL PHYSIOTHERAPY IN LABRADOR RETRIEVER DOG WITH PARAPARESIS: A CASE REPORT
Bibliographic record
Abstract
Background: Spinal cord injuries (SCI) may cause neurological problems such as muscle weakness, sensory disorders, and incontinence. This case report aims to investigate the effectiveness of the sensorimotor rehabilitative physiotherapy program in a dog with paraparesis.Case Description: A 1-year-old, 27 kg male dog was brought to Near East University, Animal Hospital, after a motor vehicle accident. The dog was diagnosed as a T13 vertebral fracture and luxation at the T13-L1 spinal level according to the clinical and radiological examination performed by a veterinary physician. The dog showed; poor standing, weakness in the hind limbs and back muscles, urinary and fecal incontinence at the clinical examination. The physiotherapy program included; massage, sensory stimulation applications, Neuromuscular Electrical Stimulation (NMES), joint mobilizations, standing-balance exercises, and gait training.Outcome Measures: Consequently, improvements were obtained in standing and sitting balance, gait, bladder, and bowel functions at the end of the seven-week treatment period. The standing duration increased from 3 sec to >60 sec; also, thigh circumferences increased from 31cm to 36 cm in the right and 32 cm to 36 cm in the left limb. Canine Acute Pain Scale score was reduced from 2 to 1 in a positive sense.Conclusion: There were a satisfying motor and functional recovery in our case. We believe that the dog’s young age and the type of injury (neurapraxia) contributed to these positive results. Therewithal, early and active physiotherapy program plays a crucial role in maintaining functional independence, also coping with the symptoms in the dog.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".