Preoperative perivascular space burden predicts treatment response to deep cervical lymphovenous anastomosis in Alzheimer's disease: A pilot study
Bibliographic record
Abstract
Background: Deep cervical lymphovenous anastomosis (DLVA) shows promise for Alzheimer's disease (AD) treatment, but patient selection criteria remain undefined. Perivascular spaces (PVS) may predict glymphatic enhancement potential. Objective: To investigate whether preoperative magnetic resonance imaging (MRI) measures of PVS burden can predict treatment response to DLVA in AD patients and explore potential mechanisms through longitudinal glymphatic function assessment. Methods: Retrospective analysis of 10 AD patients undergoing DLVA. Preoperative T1-weighted MRI quantified PVS volumes using Frangi filtering. Treatment response was assessed at one month using Mini-Mental State Examination/Montreal Cognitive Assessment improvements ≥2 points. Results: Total PVS volume demonstrated perfect predictive accuracy for treatment response (AUC = 1.000) with an optimal cut-off of 5150 mm³ (sensitivity 100%, specificity 100%). White matter PVS volume also showed strong predictive performance (AUC = 0.875, cut-off 3630 mm³, sensitivity 75%, specificity 100%). The improved group had significantly higher preoperative PVS volumes (total PVS: p = 0.012, Cohen's d = 2.631; white matter PVS: p = 0.050, Cohen's d = 1.689). Preliminary longitudinal analysis revealed divergent Analysis Along the Perivascular Space (ALPS) index changes: the improved group showed mean increase (+0.0276), while the non-improved group demonstrated decrease (-0.0252). Conclusions: Preoperative PVS burden serves as a powerful predictor of DLVA response, with higher volumes indicating sufficient anatomical reserve for therapeutic benefit. These findings establish PVS volume as clinically actionable biomarkers for precision patient selection in glymphatic-targeted AD interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".