Complications in 622 Cases of Frame-Based Stereotactic Biopsy, a Decreasing Procedure
Bibliographic record
Abstract
BACKGROUND: Frame-based stereotactic brain biopsy has played an important role in the management of patients with suspected neoplastic intracranial lesions over the last three decades. We reviewed the surgical experience of one surgeon to determine the nature and frequency of complications associated with this procedure. METHODS: Records were reviewed for 858 patients undergoing frame-based stereotactic procedures from January 1986 to May 2006. Data on each case were prospectively collected by the senior author. Procedures for Ommaya reservoir placement, brachytherapy, stereotactic craniotomy flap localization, shunt placement, or treatment of previously-diagnosed intracranial cystic lesions were excluded, leaving 614 patients in whom a total of 622 procedures were performed for purely diagnostic purposes. Complication rates and their association with clinical variables were sought. RESULTS: Morbidity and mortality rates were 6.9% (43/622) and 1.3% (8/622), respectively. The risk of symptomatic hemorrhage (intracerebral hemorrhage [ICH], subarachnoid hemorrhage [SAH], intraventricular hemorrhage [IVH]) was 4.8%. The risks of transient or permanent neurological deficits were 2.9% (18/622) and 1.5% (9/622), respectively. Biopsy of deep-seated lesions was associated with increased overall complication rate, while biopsy of Glioblastoma Multiforme (GBM) was associated with perioperative mortality. CONCLUSIONS: Overall, complication rates were comparable with those in previous reports. The subgroup of patients with deep-seated lesions or a histologic diagnosis of GBM may possess an elevated risk of overall complications or mortality, respectively, compared to other patients undergoing frame-based stereotactic brain biopsy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".