A meta-analysis of ventriculostomy-associated cerebrospinal fluid infections
Bibliographic record
Abstract
BACKGROUND: Ventriculostomy insertion is a common neurosurgical intervention and can be complicated by ventriculostomy-associated cerebrospinal fluid infection (VAI) which is associated with increased morbidity and mortality. This meta-analysis was aimed at determining the pooled incidence rate (number per 1000 catheter-days) of VAI. METHODS: Relevant studies were identified from MEDLINE and EMBASE and from reference searching of included studies and recent review articles on relevant topics. The Newcastle-Ottawa Scale was used to assess quality and risk of bias. A random effects model was used to pool individual study estimates and 95% confidence intervals (CI) were calculated using the exact Poisson method. Heterogeneity was assessed using the heterogeneity χ2 and I-squared tests. Subgroup analyses were performed and a funnel plot constructed to assess publication bias. RESULTS: There were a total of 35 studies which yielded 752 infections from 66,706 catheter-days of observation. The overall pooled incidence rate of VAI was 11.4 per 1000 catheter days (95% CI 9.3 to 13.5), for high quality studies the rate was 10.6 (95% CI 8.3 to 13) and 13.5 (95% CI 8.9 to 18.1) for low quality studies. Studies which had mean duration of EVD treatment of less than 7 days had a pooled VAI rate of 19.6 per 1000 catheter-days, those with mean duration of 7-10 days had VAI rate of 12.8 per 1000 catheter-days and those with mean duration greater than 10 days had VAI rate of 8 per 1000 catheter-days. There was significant heterogeneity for the primary outcome (p = 0.004, I-squared = 44%) and most subgroups. The funnel plot did not show evidence for publication bias. CONCLUSIONS: The incidence rate of VAI is 11.4 per 1000 catheter-days. Further research should focus on analysis of risk factors for VAI and techniques for reducing the rate of VAI.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.073 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".