P.222 Subjective pattern of postoperative neurological recovery in degenerative cervical myelopathy varies by preoperative severity of disease
Bibliographic record
Abstract
Background: Degenerative cervical myelopathy is a spinal disorder resulting in progressive spinal cord compression and consequent neurological deficits that can be assessed and tracked using the modified Japanese Orthopedic Association (mJOA) questionnaire. However, it is difficult to predict which patients will recover neurological function after surgery, making it difficult for clinicians to set reliable postoperative patient expectations. Methods: Sixty-eight operative myelopathy patients (50 male, 14 female) consented to complete the mJOA questionnaire both preoperatively and 6-months postoperatively. Fifteen of these patients had mild, twenty-three had moderate, and thirty had severe preoperative disease. Results: We found that in mild myelopathy, sensation and strength recover in similar proportions. In moderate myelopathy, a greater proportion of patients recover in each domain except for sensation. Recovery in severe myelopathy was comparable to moderate disease, but showed more dramatic recovery in sensation and sphincter function. Conclusions: This study shows that the severity of myelopathic disease influences the pattern of postoperative recovery. Though limited in sample size, the recovery patterns identified above are an important first step in recognizing myelopathy as a disease that patients experience heterogeneously both pre- and post-operatively. Our results will aid clinicians in goals-of-surgery discussions and assist with managing postoperative patient expectations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.009 | 0.002 |
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".