The cutaneous trunci reflex for localising and grading thoracolumbar spinal cord injuries in dogs
Bibliographic record
Abstract
OBJECTIVES: To evaluate the accuracy of the cutaneous trunci reflex to localise thoracolumbar spinal cord injuries and to assess the correlation between focal loss (cut-off) of the reflex and clinical severity of thoracolumbar spinal cord injury. METHODS: Prospective study of 41 dogs with thoracolumbar spinal cord injuries investigated by magnetic resonance imaging. Linear regression analysis was performed to determine the relationship between the vertebral level of the cutaneous trunci reflex cut-off and the maximal and cranial lesion extent. The association between cutaneous trunci reflex cut-off and spinal cord injury severity was tested using a Mann-Whitney U test. RESULTS: Cutaneous trunci reflex cut-off was evident in 33 (80%) of dogs. The cut-off level was 0 to 4 vertebrae caudal to the maximal spinal cord lesion in all dogs. In 16 (48.5%) dogs the cut-off was either 2 or 3 vertebrae caudal to the lesion. The presence of a cut-off significantly correlated with increasing severity (P=0.0001). Loss of the reflex occurred at less severe grades than loss of ambulation and in dogs with ambulatory paresis it was significantly (P=0.0084) associated with increasing severity. CLINICAL SIGNIFICANCE: The cutaneous trunci reflex allows localisation of thoracolumbar spinal cord lesions within four vertebrae and facilitates clinical segregation of dogs with ambulatory paresis into mild and severe categories.
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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 | 0.004 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".