P.027 Comparative effectiveness of flexible vs. rigid neuroendoscopy for ETV/CPC: a propensity score matched cohort and survival analysis
Bibliographic record
Abstract
Background: ETV/CPC has become an increasingly common technique for the treatment of infant hydrocephalus. Both flexible and rigid neuroendoscopy can be used, with little empirical evidence directly comparing the two. We, therefore, used a propensity-matched cohort and survival analysis to assess the comparative efficacy of flexible and rigid neuroendoscopy. Methods: Individual data were collected through retrospective review of infants < 2 years of age, treated at one of 2 hospitals: 1) Boston Children’s Hospital, exclusively utilizing flexible neuroendoscopy, and 2) Nicklaus Children’s Hospital, exclusively utilizing rigid neuroendoscopy. Patient characteristics and post-operative outcome were assessed. A propensity score (PS) model was developed to balance patient characteristics in the case mix. Results: A PS model was developed with 5 independent variables: chronological age, sex, hydrocephalus etiology, prior CSF diversion, and prepontine scarring. PS analysis revealed that compared to flexible neuroendoscopy, rigid neuroendoscopy had an ETV/CPC failure OR of 1.43 and 1.31 respectively, compared to unadjusted OR of 2.40. Furthermore, in a Cox regression analysis controlled by propensity score, rigid neuroendoscopy had a HR of 1.10, compared to unadjusted HR of 1.61. Conclusions: Much of the difference in ETV/CPC outcome between endoscopy types is attributed to the case mix. An observational study or randomized controlled trial is required to provide evidence-based guidelines.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".