Cerebrospinal Fluid Biomarkers as Predictors of Shunt Response in Idiopathic Normal Pressure Hydrocephalus: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The widely accepted treatment for idiopathic normal-pressure hydrocephalus (iNPH) is a cerebrospinal fluid (CSF) diversion shunt procedure, to which approximately 80% of patients will respond. The purpose of this systematic review was to identify which CSF biomarkers have been investigated in predicting shunt responsiveness in iNPH patients, and to analyze the level of evidence for each. METHODS: To find all relevant articles, a comprehensive search of Medline, Embase, and PsycINFO was conducted. RESULTS: The literature search identified 344 unique citations, of which 13 studies satisfied the inclusion criteria and were analyzed in our review. These 13 studies reported on 37 unique biomarkers. CONCLUSIONS: The available studies suggest that there is evidence for the utility of CSF biomarkers in predicting shunt responsiveness in iNPH patients, though none have been shown to predict shunt response with both high sensitivity and specificity. We found that there is no available evidence for the use of Aβ38, Aβ40, Aβ43, APL1β25, APL1β27, APL1β28, sAPP, aAPPα, sAPPβ, TNF-α, MCP-1, sCD40L, sulfatide, MBP, L-PGDS, cystatin C, transthyretin, TGF-β2, or YKL-40 in predicting shunt response. There is minimal evidence for the use of TGF-β1, TBR-II, homocysteine, and interleukins (particularly IL-1β, IL-6, and IL-10). However, the available evidence suggests that these biomarkers warrant further investigation. Aβ42, tau, p-tau, NFL, and LRG have the greatest amount of evidence for their predictive value in determining shunt responsiveness in iNPH patients. Future research should be guided by, but not limited to, these biomarkers.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".