Steroids in the Management of Preoperative Neurological Deficits in Metastatic Spine Disease: Results From the EPOSO Study
Bibliographic record
Abstract
OBJECTIVE: Patients presenting with neurological deficit secondary to metastatic epidural spinal cord compression (MESCC) are often treated with surgery in combination with high-dose corticosteroids. Despite steroids being commonly used, the evidence regarding the effect of corticosteroids on patient outcomes is limited. The objective of this study was to describe the effect of corticosteroid use on preoperative neurological function in patients with MESCC. METHODS: Patients who underwent surgery between August 2013 and February 2017 for the treatment of spinal metastases and received steroids to prevent neurologic deficits were included. Data regarding demographics, diagnosis, treatment, neurological function, adverse events, health-related quality of life, and survival were extracted from an international multicenter prospective cohort. RESULTS: A total of 30 patients treated surgically and receiving steroids at baseline were identified. Patients had a mean age of 58.2 years (standard deviation, 11.2 years) at time of surgery. Preoperatively, 50% of the patients experienced deterioration of neurological function, while in 30% neurological function was stable and 20% improved in neurological function. Lengthier steroid use did not correlate with improved or stabilized neurological function. Postoperative adverse events were observed in 18 patients (60%). Patients that stabilized or improved neurologically after steroid use showed a trend towards improved survival at 3- and 24-month postsurgery. CONCLUSION: This study described the effect of steroids on preoperative neurological function in patients with MESCC. Stabilization or improvement of preoperative neurological function occurred in 50% of the patients.
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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 | 0.002 |
| 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) | 0.000 | 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".