Accuracy of CSF Tap Test and Lumbar Infusion Test in Predicting Shunt Response in Idiopathic Normal Pressure Hydrocephalus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background Idiopathic normal pressure hydrocephalus (iNPH) presents with gait disturbance, cognitive impairment, and urinary incontinence. The cerebrospinal fluid tap test (CSF-TT) and lumbar infusion test (LIT) are commonly used to predict postoperative improvement after shunt surgery; however, their validity remains debated. Methods We performed a systematic review and meta-analysis to assess the sensitivity and specificity of CSF-TT and LIT. The protocol was registered in PROSPERO (CRD42023454502). Reporting followed the PRISMA guidelines, and certainty of evidence was assessed using the GRADE approach. Results From 1762 studies, 14 were included, reporting 697 CSF-TT and 393 LIT patients with shunt surgery as the reference standard. Considerable heterogeneity existed in test protocols, timing of assessment, and outcome measures. Several studies were retrospective with a high risk of bias. Using a bivariate random-effects model, pooled sensitivity and specificity of CSF-TT were 67.5% (95% CI 52.2–79.8, I 2 82.3%) and 53.3% (40.7-65.4, I 2 49.4%), respectively. The certainty of evidence was very low for sensitivity due to bias, inconsistency, and imprecision, and low for poor specificity due to bias and inconsistency. LIT showed a pooled sensitivity of 81% (70.3–88.3, I 2 = 27.6%) and specificity of 42.8% (20.8–68.1, I 2 = 60.8%), with moderate certainty for sensitivity and poor specificity. Conclusion Both CSF-TT and LIT demonstrate only modest accuracy in predicting shunt outcomes. The pooled specificity of the CSF-TT is similar to a coin toss, limiting its standalone clinical utility. The moderate certainty regarding the poor specificity of the LIT highlights the need for improved prognostic models. These tests should be interpreted in conjunction with other clinical and imaging findings, rather than in isolation. We need standardized, high-quality studies to better define their diagnostic value and support shared decision-making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.045 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".