Intracranial Hemorrhage following Spinal Surgery: A Systematic Review of a Rare Complication
Bibliographic record
Abstract
Abstract Introduction Intracranial hemorrhage (ICH) is a potentially severe complication of spinal surgeries. The occurrence of such complications causes deterioration of the patient's clinical status and delayed discharge from the hospital. Although no specific etiological factors were identified for this complication, but multiple risk factors might play role in its development, they include the use of anticoagulants, presence of uncontrolled hypertension, and perioperative patient positioning. Aim A systematic review of the literature to investigate the prevalence of different types of intracranial hemorrhages in patients who underwent spinal surgeries. Methods A literature review was conducted using multiple research databases. Data were extracted using multiple variables that were formulated incongruent with the study aim and then further analyzed. Results A total of 79 studies were included in our analysis after applying the exclusion criteria and removing of repeated studies, 109 patients were identified where they were diagnosed with intracranial hemorrhage after spine surgery with a mean age of 54 years. The most common type of hemorrhage was cerebellar hemorrhage (56.0%) followed by SDH and intraparenchymal hemorrhage; 23.9 and 17.4%, respectively. The most common spine surgery was laminectomy (70.6%), followed by fixation and fusion (50.5%), excision of spinal lesions was done in 20.2% of the patient, and discectomy (14.7%). Conclusion The data in this study showed that out of 112 patients with ICH, cerebellar hemorrhage was the most common type. ICH post–spine surgery is a rare complication and the real etiologies behind this complication are still unknown, cerebrospinal fluid drain and durotomy were suggested.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".