P.168 Radiographic factors associated with success of endoscopic third ventriculostomy (ETV): retrospective cohort study
Bibliographic record
Abstract
Background: This retrospective cohort study investigates radiographic factors linked to the success of Endoscopic Third Ventriculostomy (ETV) for hydrocephalus. Methods: We examined 48 patients who underwent ETV between August 2011 and March 2023. Radiographic factors analyzed included the basal skull angle, modified basal skull angle, interpeduncular cistern diameter, prepontine diameter, and approach angle to the third ventricle floor. Statistical analysis was performed using R studio. Results: The cohort had a median age of 41 years, with 22 females. Pathologies included aqueductal stenosis (21 cases), tectal tumors (7), and IVH (5). The mean ETV Success Score (ETVSS) was 76.7. Of the 21 failures, 16 required a shunt. A strong correlation was found between ETVSS and procedure success (p<0.001). Modified basal skull angle (p=0.028), interpeduncular cistern diameter (p<0.001), and approach angle (p<0.001) were all associated with ETV success. Decision tree analysis showed that the inclusion of approach angle to ETVSS improved sensitivity and specificity, reaching 1.0 for both. Conclusions: In conclusion, the study highlights that radiographic factors, particularly the modified basal skull angle, interpeduncular cistern diameter, and approach angle, are key predictors of ETV success. This information can assist neurosurgeons in planning cases more effectively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".