Surgical aspiration versus excision for intraparenchymal abscess: a systematic review and Meta-analysis
Bibliographic record
Abstract
Brain abscesses are associated with considerable morbidity and mortality, requiring timely intervention to achieve favourable outcomes. With the advent of high-resolution computed tomography (CT) imaging, mortality following both aspiration and excision of brain abscesses has improved markedly. As a result, there has been a marked shift in neurosurgical practice with aspiration eclipsing excision as the favoured first-line modality for most abscesses. However, this trend lacks sufficient supporting evidence, and this systematic review and meta-analysis seeks to compare aspiration and excision in the treatment of brain abscess. Twenty-seven studies were included in the systematic review, and seven comparative papers in meta-analysis. Aspiration was the chosen technique for 67.5% of patients. Baseline characteristics from the studies included only in the systematic review demonstrated that abscesses treated by aspiration were typically larger and in a deeper location than those excised. In the meta-analysis, we initially found no significant difference in mortality, re-operation rate, or functional outcome between the two treatment modalities. However, sensitivity analysis revealed that excision results in lower re-operation rate. On average, the included studies were of poor quality with average Methodological Index for Non-Randomized Studies (MINORS) scores of 10.3/16 and 14.43/24 for non-comparative and comparative papers respectively. Our study demonstrates that excision may offer improved re-operation rate as compared to aspiration for those abscesses where there is no prior clinical indication for either modality. However, no differences were found with respect to mortality or functional outcome. Evidence from the literature was deemed low quality, emphasizing the need for further investigation in this field, specifically in the form of large, well-controlled, comparative trials.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".