Predictors of Outcome Following Cerebral Aqueductoplasty
Bibliographic record
Abstract
BACKGROUND: The evidence supporting the efficacy and safety of cerebral aqueductoplasty (CA) is limited to small surgical series. OBJECTIVE: To perform an individual participant data meta-analysis to determine the efficacy and safety of CA and to determine the effect of patient's age, etiology, surgical approach, and use of stent on success. METHODS: Electronic databases (MEDLINE, EMBASE, and CINAHL) were searched with no language or date restrictions to identify cohort studies of consecutive participants undergoing CA (without concomitant endoscopic third ventriculostomy or cerebrospinal fluid [CSF] shunt) that reported outcome. Outcome was defined as the time elapsed from the index operation until a second procedure was performed for CSF diversion. RESULTS: Of 146 citations, 14 articles reporting on 137 participants were eligible. One hundred three participants (75%) did not require a second CSF diversion procedure. The mean duration until repeat CSF diversion procedure was 121.6 months (95% confidence interval [CI], 102.2-141.0). In multivariate analysis, older age at surgery (hazard ratio [HR], 0.43; 95% CI, 0.21-0.88; P = .020), congenital etiology (HR, 0.18; 95% CI, 0.04-0.85; P = .030), and use of stent (HR, 0.30; 95% CI, 0.13-0.70; P = .006) were independent predictors of good outcome. Morbidity, mainly ophthalmoparesis and hemorrhage, was experienced in 22% of participants. CONCLUSION: Small retrospective cohort studies are inherently prone to biases, some of which are overcome through the use of individual participant data. The best available evidence suggests that CA is an effective procedure with a moderate morbidity profile. Older age at surgery, congenital etiology, and use of stent predict a good outcome with respect to delaying the requirement for a second CSF diversion procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".