Research priorities for improving cognitive and neuropsychological outcomes in hydrocephalus
Bibliographic record
Abstract
Hydrocephalus is a neurological disorder that impacts approximately 85 per 100,000 individuals worldwide and is associated with motor and cognitive impairments. While many advances in surgical interventions have helped substantially improve the survival rates and quality of life of those affected, there continues to be significant gaps in our understanding of the etiology of this heterogeneous condition as well as its specific neuropsychological and functional challenges across different phases of life. To address these limitations, the Hydrocephalus Association and Rudi Schulte Research Institute organized a workshop titled, "Improving Cognitive and Psychological Outcomes in Hydrocephalus", composed of top academics in the fields of hydrocephalus, cognition, and neuropsychology, as well as individuals with hydrocephalus or their caregivers. The purpose was to review the available evidence and propose pertinent areas of further research to improve the cognitive functioning, functional status, and quality of life of individuals with hydrocephalus. These topics included cognitive and neuropsychological assessments and daily-life function of children and adults living with hydrocephalus, biomarkers of cognitive function, animal modeling of hydrocephalus, and the longitudinal impact of hydrocephalus treatment. The following paper outlines four primary areas that warrant research: (1) neuropsychological phenotypes, (2) treatment-focused research considerations, (3) translational pre-clinical tools, and (4) establishing pathways for longitudinal care. Through the efforts of this group, the goal of this manuscript is to inspire and direct scientific and clinical inquiry towards these noted research priorities to further improve the lives of individuals with hydrocephalus and their families.
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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".