Ten Steps for NPH Management: Advancements in Diagnosis and Treatment of Adult Hydrocephalus
Bibliographic record
Abstract
Abstract Objectives The authors of the present study intend to describe a straightforward protocol for normal pressure hydrocephalus diagnosis and management, with the employment of a multidisciplinary team approach effort. Methods Using a strict methodological approach for initial diagnosis, taking into consideration occupational therapy and physical therapy assessment, the authors have set out to elaborate a simple protocol for suspicion and, once diagnosed, treatment of normal pressure hydrocephalus. We have used the MoCA (Montreal Cognitive Assessment) and walking assessment that included speed, independence, and distance (SID), 10 m walk test, TUG (timed up and go) evaluation, 6-minute Walk Test, MiniBESTest, as the main factors for pre and post lumbar drainage assessment, after which, the alternatives were deliberated and followed, or not, by ventriculoperitoneal shunt insertion. Results The authors have described a protocol, consisting of ten easy steps, which involves a multidisciplinary team, including occupational therapy and physical therapy professionals, as well as neurologists and neurosurgeons for improved and objective assessment prior to insertion of lumbar drain and, thereafter, detecting the population at most benefit for ventriculoperitoneal shunt insertion. We have described the Ten Step Approach for Normal Pressure Hydrocephalus management, including from initial clinical presentation and imaging, to pre and post lumbar drainage, for lastly deciding upon necessity for ventriculoperitoneal shunt insertion. Conclusions A straightforward protocol for normal pressure hydrocephalus seems not only feasible, but simple to implement in most neurosurgical departments, with good accuracy of prediction of lumbar drainage assessment to shunting outcomes.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".