Outcomes and prognostic indicators in 59 paraplegic medium to large breed dogs with extensive epidural hemorrhage secondary to thoracolumbar disc extrusion
Bibliographic record
Abstract
OBJECTIVE: To evaluate outcomes and prognostic factors after decompressive hemilaminectomy in paraplegic medium to large breed dogs with extensive epidural hemorrhage (DEEH) and thoracolumbar intervertebral disc extrusion (TL-IVDE). STUDY DESIGN: Retrospective, cohort, descriptive study. ANIMALS: Fifty-nine client-owned dogs. METHODS: Medical records and advanced imaging were reviewed for paraplegic dogs with DEEH. Ambulatory status 6 months after surgery and postoperative complications were recorded. Multiple logistic regression models were constructed to explore prognostic factors. RESULTS: Records of 22 dogs with and 37 dogs without pelvic limb pain perception at presentation were included. Median age of dogs was 5 years (interquartile range, 4-7), and mean weight was 26.9 kg (SD, ±9.71). Labradors and Labrador mixes were most common (17/59 [28.8%]). Recovery of ambulation occurred in 17 of 22 (77.3%) dogs with and in 14 of 37 (37.8%) dogs without pain perception prior to surgery. Progressive myelomalacia was recorded in three of 59 (5.1%) dogs, one with pain perception and two without pain perception at presentation. Postoperative complications (14/59 [23.7%]) were common. Factors independently associated with outcome included clinical severity (odds ratio [OR] 0.179, P = .005), number of vertebrae with signal interruption in half Fourier single-shot turbo spin-echo sequences (HASTEi; OR, 0.738; P = .035), and ratio of vertebral sites decompressed to HASTEi (OR, 53.79; P = .03). CONCLUSION: Paraplegic medium to large breed dogs with DEEH have a less favorable outcome after surgical decompression than paraplegic dogs with TL-IVDE. CLINICAL SIGNIFICANCE: Dogs with DEEH can have severe postoperative complications. Loss of pain perception and increased HASTEi are associated with a poor outcome, while more extensive decompression improves outcome.
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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 | 0.002 |
| 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 | 0.001 |
| 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".