P.202 Image-Guidance for Ventricular Drains Insertion: A Systematic Review and Metanalysis
Bibliographic record
Abstract
Background: The use of Image-guidance to improve the accuracy during ventricular drain insertion has been attempted. We aim to assess the effect of use of Image-guidance on accuracy, drain failure rate and number of ventricular cannulation attempts. Methods: MEDLINE, EMBASE and Cochrane Library databases were searched from inception to February 2021 looking for studies comparing image-guided versus freehand ventricular drain insertion. Two reviewers independently screened studies, extracted data and assessed risk of bias and quality of evidence. Metanalysis was conducted in compliance with PRISMA guidelines using a random-effects model and GRADE tool was used to assess quality of evidence. Results: 17 studies with 3404 patients were included, all of which were of non-randomized design. Pooled data on drain accuracy and drain failure rates showed favourable effect of image-guidance with risk ratio of 1.31 (95% CI of 1.13 – 1.51, low quality evidence) and 0.63 (95% CI 0.48 – 0.83, moderate quality evidence), respectively. Pooled data were equivocal for number of attempts with mean difference score of -0.11 times (95% CI -0.31 – 0.09, very low-quality evidence). Conclusions: Image-guidance likely enhances drain accuracy and reduces drain failure rate. No clear recommendation can be drawn on the benefit of intervention on number of drain insertion attempts.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".