Pathologic classification of a late‐onset peripheral neuropathy in a spontaneous Labrador retriever dog model
Bibliographic record
Abstract
Late-onset peripheral neuropathy (LPN) is a heritable canine neuropathy commonly found in Labrador retrievers and is characterized by laryngeal paralysis and pelvic limb paresis. Our objective was to establish canine LPN as a model for human hereditary peripheral neuropathy by classifying it as either an axonopathy or myelinopathy and evaluating length-dependent degeneration. We conducted a motor nerve conduction study of the sciatic and ulnar nerves, electromyography (EMG) of appendicular and epaxial musculature, and histologic analysis of sciatic and recurrent laryngeal nerves in LPN-affected and control dogs. LPN-affected dogs exhibited significant decreases in compound muscle action potential (CMAP) amplitude, CMAP area, and pelvic limb latencies. However, no differences were found in motor nerve conduction velocity, residual latencies, or CMAP duration. Distal limb musculature showed greater EMG changes in LPN-affected dogs. Histologically, LPN-affected dogs exhibited a reduction in the number of large-diameter axons, especially in distal nerve regions. In conclusion, LPN in Labrador retrievers is a common, spontaneous, length-dependent peripheral axonopathy that is a novel animal model of age-related peripheral neuropathy that could be used for fundamental research and clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".