Gait Analysis in Idiopathic Normal Pressure Hydrocephalus: A <scp>Meta‐Analysis</scp>
Bibliographic record
Abstract
Background: Gait analysis objectively quantifies gait impairment in idiopathic normal pressure hydrocephalus (iNPH), may improve diagnosis and evaluation for surgical candidacy. Objectives: This meta-analysis aims to understand which objective gait parameters improve after tap-test (TT) and CSF shunt surgery (CSS), also comparing responders (R) with non-responders (NR) and to assess if gait restores within the range of healthy controls after procedures. Methods: Studies enrolling iNPH with at least one instrumented gait measure were selected. Three time points of gait assessment were defined: PRE, POST-TT, and POST-CSS. Gait velocity, cadence, step length, stride length, and double limb support time were evaluated. Patients were categorized based on responsiveness to CSF diversion procedures. Results: Seventeen studies including 527 patients were selected. iNPH improved significantly in almost all gait parameters POST-TT, and to a greater extent POST-CSS. Gait parameters consistently discriminated iNPH from healthy controls. Despite the aforementioned improvements, iNPH's gait did not completely normalize after CSF diversion procedures. Meta-regression analysis also revealed that TT's effect on gait velocity plateaus after 24-48 hr and returns to baseline in 90-100 hr. Conclusions: Gait analysis is a reliable quantitative instrument to assess gait impairment in iNPH, demarking a net differentiation from healthy controls, according to the notion that the iNPH CSF dynamic alteration also leads to an irreversible damage. Specific gait parameters improve among TT-R, providing an opportunity to select patients that will respond to CSS. Future studies validating a standardized reporting method including criteria of responsiveness, specific gait parameters, and timeframe of assessment are needed.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.019 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".