Exploration of clinical predictors of the degree of ventricular catheter obstruction: a multicenter retrospective study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore how clinical factors, including the number of lifetime revision surgeries and the duration of implantation, affect the degree of obstruction and failure rates of ventricular catheters (VCs) used to manage hydrocephalus. METHODS: A total of 343 VCs and their associated clinical data, including patient demographics, medical history, and surgical details, were collected from 5 centers and used for this analysis. Each VC was classified by the degree of obstruction after macroscopic analysis. Univariate, multivariate, and binned analyses were conducted to test for associations between clinical data and degree of VC obstruction. RESULTS: VCs from patients with 0 to 2 lifetime revisions had a larger proportion of VC holes obstructed than VCs from patients with 10 or more revisions (p = 0.0484). VCs implanted for less than 3 months had fewer obstructed holes with protruding tissue aggregates than VCs implanted for 13 months or longer (p = 0.0225). Neither duration of implantation nor the number of lifetime revisions was a significant predictor of the degree of VC obstruction in the regression models. In the multinomial regression model, contact of the VCs with the ventricular wall robustly predicted the overall obstruction status of a VC (p = 0.005). In the mixed-effects model, the age of the patient at their first surgery emerged as a significant predictor of obstruction by protruding tissue aggregates (p = 0.002). VCs implanted through the parietal entry site were associated with more holes with nonobstructive growth and fewer empty holes than VCs implanted via other approaches (p = 0.001). CONCLUSIONS: The number of lifetime revisions and duration of implantation are correlated with the degree of VC obstruction but do not predict it. Contact of the VC with the ventricular wall and the age of the patient at their first surgery are predictors of the degree of VC obstruction, while the entry site of the VC correlates with it.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".