P.157 Frame-based stereotactic brain biopsy: A retrospective review of diagnostic yield and complications at a Canadian Center
Bibliographic record
Abstract
Background: Historically, frame-based stereotactic brain biopsy (SBB) has played an important role in the diagnosis of intracranial lesions. We performed a single centre analysis of the outcomes and efficacy of SBB at the London Health Sciences Centre (LHSC). Methods: We performed a retrospective chart review of frame-based SBB from 2006 to 2017 at the LHSC. Intra-operative and final pathology reports were analyzed for biopsy diagnosis and the diagnosis was compared with pre-operative neuroimaging reports for correlation. SBB-associated morbidity and mortality were investigated using chart review and post-operative neuroimaging. Results: 173 consecutive patients were identified. The overall morbidity rate was 8.7% (15 cases) and mortality rate was 0.6% (1 case). Final biopsy diagnostic accuracy was 96%, intra-operative diagnostic accuracy was 94% and pre-operative imaging diagnostic accuracy was 65%. Elevated partial thromboplastin time and the presence of hemorrhage on post-operative CT were associated with neurological morbidity and mortality. The need to obtain three or greater samples the time of biopsy was associated with non-diagnostic biopsy. Conclusions: At the LHSC, SBB is a relatively safe and effective surgical procedure with high diagnostic yield and relatively low risk of complications. Intra-operative pathology has a high efficacy in determining diagnosis when compared to final pathology.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".