Residual volume of extruded disc material and residual spinal cord compression measured on postoperative MRI do not predict neurological outcomes in dogs following decompressive surgery for thoracolumbar intervertebral disc extrusion
Bibliographic record
Abstract
Published studies on the validity of using quantitative MRI measures of pre- and postoperative spinal cord (SC) compression as prognostic indicators for dogs undergoing surgery for intervertebral disc extrusion (IVDE) are currently limited. The aim of this retrospective analytical study was to describe the volume of postoperative residual extradural material (VREM) and the ratio of the cross-sectional area (CSA) of maximum SC compression to the CSA of SC in a compression-free intervertebral space as MRI measures of preoperative and postoperative compression (residual spinal cord compression, RSCC), and to compare these measures between the neurological outcome in a group of dogs. Inclusion criteria were dogs that underwent surgery for thoracolumbar IVDE, were imaged pre- and immediately postoperatively by MRI, and had a neurological follow-up examination 2 to 5 weeks postoperatively. Two blinded observers independently performed measurements in pre- and postoperative MRI studies. Dogs were classified into positive outcome (PO) and negative outcome (NO) groups based on follow-up neurologic examination scores. Seventeen dogs were included (12 PO, 5 NO). Interobserver agreement for MRI measurements was good to excellent (ICCs: 0.76-0.97). The prevalence of residual extradural material in postoperative MRI studies was 100%. No significant differences in mean preoperative SC compression, mean RSCC, mean SC decompression, or VREM were found between outcome groups (P = .25; P = .28; P = .91, P = .98). In conclusion, neither postoperative VREM nor RSCC could predict successful neurological outcomes.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".