Current Practice Patterns Among Indian Neurologists in the Evaluation and Management of Normal Pressure Hydrocephalus: A Nationwide Cross-Sectional Survey
Bibliographic record
Abstract
INTRODUCTION: This cross-sectional study explores the diagnostic and management practices of normal pressure hydrocephalus (NPH) in India. METHODS: A 16-question online survey based on the Checklist for Reporting Results of Internet E-Surveys checklist was disseminated via closed social media and academic groups. RESULTS: A total of 59 neurologists responded. There was significant variability in how the cerebrospinal fluid (CSF) tap test (TT) was implemented. Although 95% of practitioners performed a CSF TT before referring patients for surgery, there were differences in the volume of CSF removed, the assessment tools used, thresholds, and the timing of post-test evaluations. Notably, 36% of neurologists relied on subjective assessment instead of objective scoring to determine CSF TT responsiveness. One-third still considered surgical referral even if the CSF TT was negative, especially when imaging was strongly suggestive of the diagnosis. CONCLUSION: This study highlights considerable heterogeneity in the evaluation of NPH in India. We need evidence-based practice parameters to ensure accurate diagnosis, informed treatment decisions, and improved patient 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".