Gait parameters, Imaging features, and CSF tap test in idiopathic Normal Pressure Hydrocephalus (iNPH) Is composite testing the way to go?
Bibliographic record
Abstract
Abstract Introduction Idiopathic normal pressure hydrocephalus is characterized by gait disturbance, cognitive decline, and urinary dysfunction which may improve with ventriculo-peritoneal shunting. This study evaluated the role of conventional MRI features and the CSF tap test (CSF-TT) in predicting shunt responsiveness in iNPH. Methods This is an Ambispective cohort study of 40 patients with probable iNPH evaluated between 2019 and 2024. Baseline MRI parameters, gait features, iNPH score, CSF opening pressure were analyzed. Functional outcome was assessed using the modified Rankin scale (mRS) at baseline, 24 hours after CSF-TT, and 24 weeks after VP shunt surgery. CSF-TT responders were defined as at least a 1-point improvement in mRS 24 hours after CSF-TT. The diagnostic performance of individual MRI parameters and composite diagnostic parameters were evaluated. Results Forty patients underwent CSF-TT. There were no significant differences between CSF-TT responders and non-responders in the baseline clinical gait parameters, the iNPH scale, and MRI findings. Turning disturbance, wide-based stride, and reduced foot clearance showed significant improvement after CSF-TT. Individual MRI parameters and CSF-TT parameters showed limited value in predicting shunt responsiveness. Composite diagnostic criteria combining CSF-TT and CSF opening pressure >18cm H20 showed sensitivity of 62.5 % and specificity of 71.4% with the highest Youden index indicating modest diagnostic accuracy in predicting shunt responsiveness. Conclusion Shunt surgery provided significant functional benefit in iNPH. Neither the CSF tap test nor conventional MRI markers alone reliably predicted shunt responsiveness. A multimodal assessment combining imaging, clinical evaluation, and CSF dynamics is required to optimize patient selection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".