Abstracts from Hydrocephalus 2024: The 16th Meeting of the Hydrocephalus Society
Bibliographic record
Abstract
Introduction: Despite generally good technical progress in controlling intraocular pressure (IOP) in ophthalmology or ICP (intracranial pressure) in neurosurgery, it is known that some patients (about 25% of the targeted populations) still suffer from experiencing visual impairments or symptoms of dementia.This presents a dilemma for ophthalmologists, neurosurgeons and their patients.Methods: A literature search was conducted using the keywords "normal pressure hydrocephalus" AND "normal pressure glaucoma" AND "neurodegeneration OR neuroprotection" to determine whether there are any promising treatments for protecting from neurodegeneration that could give hope to patients.Results: 117 articles were found on PubMed and Google Scholar; after exclusion, 72 articles were screened, further exclusions were made when no reference to treatment options were reported.Treatment optionsincluded sleep, breathing, clean air, without fine particles; physical activity, posture, reduction of cardiovascular risk factors including adiposity; 1 Hz flicker light and electromagnetic stimulation; nutritional supplements, including polyunsaturated fatty acids (PUFA,) Vitamin B3, flavenoids; GABA agonists, brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF), mitochondrial and endoplasmic reticulum-associated treatments, those directed against ferroptosis and inflammation, and others seem in development. Conclusions:The evidence for a variety of different treatments available should encourage both physicians and patients to engage in the treatment process to delay blindness or dementia, or both.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.024 |
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".