C.03 Surgical complications with and without image guidance: meta-analysis of Ommaya reservoir insertions
Bibliographic record
Abstract
Background: There remains an important role for consolidating evidence on the utility of image guidance (IG) in neurosurgery. In 1963, Ayub Ommaya proposed a surgical technique for the -placement of a subcutaneous reservoir and pump to allow access to intraventricular cerebrospinal fluid. In this study, we sought to compile evidence from the literature about surgical outcome in ORI with and without IG. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Overall surgical complication rate was considered a primary outcome and further classified into specific complication categories. Results: 40 studies were identified, including our own series, for a total of 1947 independent ORI procedures. Pooled rates of outcome for IG compared to non-IG were 6.0% versus 13.6% for overall complications; 2.0% versus 2.8% for catheter malfunction; 1.9% versus 2.3% for catheter malposition; 0.5% versus 4.0% for early infection; 4.3% versus 9.4% for any infection; and 0.4% versus 1.4% for mortality. Conclusions: We observed that IG ORI resulted in improved accuracy and decreased complications compared to non-IG. To our knowledge, this study comprises the largest observational analysis of operative outcomes demonstrating evidence for the utility of IG.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.037 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".