Diagnostic Accuracy of CSF Tap-Test Parameters in Predicting Shunt Responsiveness in Normal Pressure Hydrocephalus: A Cohort Study
Bibliographic record
Abstract
Abstract Background The cerebrospinal fluid tap test (CSF-TT) is widely used as an ancillary test to select patients with idiopathic Normal Pressure Hydrocephalus (iNPH) for surgical management; however, its diagnostic utility remains unclear. We evaluate the diagnostic utility of CSF-TT in predicting outcomes following shunt surgery. Methods Patients with possible or probable iNPH underwent assessments of gait, urinary, and cognitive function using Boon’s gait scale, iNPH grading scale, TUG score, MoCA, and modified Rankin Scale (mRS) at baseline and 24 hours after CSF-TT. They were offered VP shunt surgery based on clinico-radiological profile, regardless of CSF-TT outcome. Post-operative outcomes were evaluated at 24 weeks, and those who showed a ≥1-point improvement in the mRS were classified shunt responders. The diagnostic performance of various scales was assessed using receiver operating characteristic curves. Results Out of 24 patients who underwent shunting 15 (62.5%) were classified as shunt responders. A one-point reduction in the modified Rankin scale at 24 hours post-CSF-TT had a sensitivity of 53.3% (95% CI: 26.6-78.7) and specificity of 66.7% (95% CI: 29.9-92.5) in predicting shunt responsiveness. Changes in the iNPH score, TUG test score, percentage change in TUG score, Boon’s gait score, and percentage change in Boon’s gait score were not valid predictors since the confidence intervals of the AUROCs crossed 0.5. Conclusion CSF-TT parameters show limited accuracy in predicting shunt responsiveness in patients with iNPH. Our results suggest that about one-third of positive tap tests were false positives, and the test’s sensitivity was only slightly better than random chance in predicting post-operative shunt outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".