Magnetization transfer and diffusion tensor imaging in dogs with intervertebral disk herniation
Bibliographic record
Abstract
Abstract Background Quantitative magnetic resonance imaging (QMRI) techniques of magnetization transfer ratio (MTR) and diffusion tensor imaging (DTI) provide microstructural information about the spinal cord. Objective Compare neurologic grades using the modified Frankel scale with MTR and DTI measurements in dogs with thoracolumbar intervertebral disk herniation (IVDH). Animals Fifty-one dogs with thoracolumbar IVDH. Methods Prospective cohort study. Quantitative MRI measurements of the spinal cord were obtained at the region of compression. A linear regression generalized estimating equations model was used to compare QMRI measurements between different neurological grades after adjusting for age, weight, duration of clinical signs, and lesion location. Results Grade 5 (.79 × 10−3 mm2/s [median], .43−.91 [range]) and axial (1.47 × 10−3 mm2/s, .58−1.8) diffusivity were lower compared to grades 2 (1.003, .68−1.36; P = .02 and 1.81 × 10−3 mm2/s, 1.36−2.12; P < .001, respectively) and 3 (1.07 × 10−3 mm2/s, .77−1.5; P = .04 and 1.92 × 10−3 mm2/s, 1.83−2.37;P < .001, respectively). Compared to dogs with acute myelopathy, chronic myelopathy was associated with higher mean (1.02 × 10−3 mm2/s, .77−1.36 vs. .83 × 10−3 mm2/s, .64−1.5; P = .03) and radial diffusivity (.75 × 10−3 mm2/s, .38−1.04 vs. .44 × 10−3 mm2/s, .22−1.01; P = .008) and lower MTR (46.76, 31.8−56.43 vs. 54.4, 45.2−62.27; P = .004) and fractional anisotropy (.58, .4−0.75 vs. .7, .46−.85; P = .02). Fractional anisotropy was lower in dogs with a T2-weighted intramedullary hyperintensity compared to those without (.7, .45−.85 vs. .54, .4−.8; P = .01). Conclusion and Clinical Relevance Mean diffusivity and AD could serve as surrogates of severity of spinal cord injury and are complementary to the clinical exam in dogs with thoracolumbar IVDH.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".