Factors predicting outcome of surgical treatment of spontaneous spinal hematomas: a retrospective cohort study in four tertiary reference centers
Bibliographic record
Abstract
BACKGROUND: Spontaneous spinal extradural hematoma (SSEH) is a rare but disabling disorder. Most of the previous assumptions regarding the factors that contribute to poor neurological recovery from SSEH are based on small case samples or conditions with similar clinical presentations but different physiopathologies. Our goal was to find the most relevant prognostic factors for neurological recovery in patients suffering SSEH treated with surgical evacuation. METHODS: From a retrospective database of 29 surgical patients with SSEH, several clinical and radiological variables were recorded. These variables were compared between patients with good and poor neurological recovery, considering good as an improvement by at least one point in the ASIA Scale. RESULTS: Among the patients included, morbidity and mortality rate was 6.9% and 3.4%, respectively, with a mean follow-up of 7.1 months. Neurological full recovery was experienced by 33% of the patients included, and 86% of individuals had an improvement in their neurological condition at last follow-up. Lesser intramedullary lesions were significantly associated with greater chances of improvement in ASIA Scale at discharge and at follow-up. Surgical decompression within the first 24 hours of onset of symptoms were correlated with better neurological outcomes at follow-up. CONCLUSIONS: MRI is a powerful tool to predict the neurological outcome in SSEH patients, and it should be considered as an another resource to better know the patients with greater chances of having neurological recovery, especially in cases where the neurological examination is not reliable at the initial exam.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".