Standardizing the large-volume "tap test" for evaluating idiopathic normal pressure hydrocephalus: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Idiopathic normal pressure hydrocephalus (iNPH) is characterized by the clinical triad of gait, cognitive, and urinary dysfunction associated with ventriculomegaly on neuroimaging. Clinical evaluation before and after CSF removal via large volume lumbar puncture (the "tap test") is used to determine a patient's potential to benefit from shunt placement. Although clinical guidelines for iNPH exist, a standardized protocol detailing the procedural methodology of the tap test is lacking. EVIDENCE ACQUISITION: Using PRISMA guidelines, a systematic review of PubMed and Embase identifying studies of the tap test in iNPH was performed, centered on four clinical questions (volume of CSF to remove, type of needle for lumbar puncture, which clinical assessments to utilize, and timing of assessments). A modified Delphi approach was then applied to develop a consensus standardized tap test protocol for the evaluation of idiopathic normal pressure hydrocephalus. EVIDENCE SYNTHESIS: Two hundred twenty-two full-text articles encompassing a total of 80,322 participants with iNPH met eligibility and were reviewed. Variations in the tap test protocol resulted in minimal concordance among studies. A standardized protocol of the tap test was iteratively developed over a two-year period by members of the International Parkinson and Movement Disorders Society Normal Pressure Hydrocephalus Study Group until expert consensus was reached. CONCLUSIONS: The literature shows significant variability in the procedural methodology of the tap test. The proposed protocol was subsequently developed to standardize clinical management, improve patient outcomes, and better align future research in idiopathic normal pressure hydrocephalus.
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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 | 0.030 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".