Radiolological predictors of recurrence of chronic subdural hematoma
Bibliographic record
Abstract
OBJECTIVE: Chronic subdural hematoma is one of the most common clinical entities encountered in daily neurosurgical practice. Considerable recurrence rates have been reported for chronic subdural hematoma following surgical evacuation. Many studies have suggested various radiological factors that may be associated with the recurrence of CSDH. However, the results are inconsistent. This study focuses on determining the radiological factors predictive of chronic subdural hematoma recurrence. METHODS: A retrospective analysis of 113 patients diagnosed with chronic subdural hematoma who were surgically treated between August 2013 and December 2014 was performed. The radiological features were analyzed to clarify the correlation between these radiological factors and postoperative recurrence of chronic subdural hematoma. RESULTS: Twenty patients (17.7%) experienced recurrence. Chronic subdural hematoma recurrence was found to be significantly associated (p<0.05) with preoperative hematoma thickness ≥ 20 mm. Midline shift, hematoma density and bilaterality were not significantly associated with recurrence. Post operative drainage also significantly (p<0.05) reduced chronic subdural hematoma recurrence. CONCLUSION: Preoperative hematoma thickness ≥ 20 mm is an independent predictor of recurrence of chronic subdural hematoma. Postoperative drainage also significantly reduces chronic subdural hematoma recurrence.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".