Use of cortical volume to predict response to temporary CSF drainage in patients with idiopathic normal pressure hydrocephalus
Bibliographic record
Abstract
OBJECTIVE: Temporary drainage of CSF with lumbar puncture or lumbar drainage has a high predictive value for identifying patients with suspected idiopathic normal pressure hydrocephalus (iNPH) who may benefit from ventriculoperitoneal shunt insertion. However, it is unclear what differentiates responders from nonresponders. The authors hypothesized that nonresponders to temporary CSF drainage would have patterns of reduced regional gray matter volume (GMV) as compared with those of responders. The objective of the current investigation was to compare regional GMV between temporary CSF drainage responders and nonresponders. Machine learning using extracted GMV was then used to predict outcomes. METHODS: This retrospective cohort study included 132 patients with iNPH who underwent temporary CSF drainage and structural MRI. Demographic and clinical variables were examined between groups. Voxel-based morphometry was used to calculate GMV across the brain. Group differences in regional GMV were assessed and correlated with change in results on the Montreal Cognitive Assessment (MoCA) and gait velocity. A support vector machine (SVM) model that used extracted GMV values and was validated with leave-one-out cross-validation was used to predict clinical outcome. RESULTS: There were 87 responders and 45 nonresponders. There were no group differences in terms of age, sex, baseline MoCA score, Evans index, presence of disproportionately enlarged subarachnoid space hydrocephalus, baseline total CSF volume, or baseline white matter T2-weighted hyperintensity volume (p > 0.05). Nonresponders demonstrated decreased GMV in the right supplementary motor area (SMA) and right posterior parietal cortex as compared with responders (p < 0.001, p < 0.05 with false discovery rate cluster correction). GMV in the posterior parietal cortex was associated with change in MoCA (r2 = 0.075, p < 0.05) and gait velocity (r2 = 0.076, p < 0.05). Response status was classified by the SVM with 75.8% accuracy. CONCLUSIONS: Decreased GMV in the SMA and posterior parietal cortex may help identify patients with iNPH who are unlikely to benefit from temporary CSF drainage. These patients may have limited capacity for recovery due to atrophy in these regions that are known to be important for motor and cognitive integration. This study represents an important step toward improving patient selection and predicting clinical outcomes in the treatment of iNPH.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".