Granuloma formation as a late complication of burr-hole surgery for chronic subdural hematoma
Bibliographic record
Abstract
BACKGROUND: Surgical treatment remains the mainstream therapeutic regimen for chronic subdural hematoma (CSDH), and burr-hole craniostomy with subdural drainage is the preferable approach. Herein, we reported a case of intracranial granuloma formation as a late complication of burr-hole surgery for CSDH. CASE PRESENTATION: A 31-year-old man presented with a 1-month history of headache. Head computed tomography (CT) showed a subdural hematoma in the left frontal-temporal-parietal region with significant midline shifting. A burr-hole evacuation of the hematoma with closed-system drainage was performed. CT obtained immediately after the surgery demonstrated that the hematoma was mostly evacuated. Nine months later, he presented to us again due to intermittent headache in the left temporoparietal region. Brain magnetic resonance imaging revealed a space-occupying mass at the site of the original hematoma. A bone-flap craniotomy was performed for resecting the mass. Histopathological examination revealed a granuloma. The microbial cultivation of the resected specimen was negative. The postoperative course was uneventful, and the headache was relieved. CONCLUSION: Granuloma formation is an extremely rare late complication of burr-hole surgery for CSDH. Physicians involved in the perioperative management of CSDH should be aware of this condition, and bone-flap craniotomy may be warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".