The Eastern Quebec Study on Idiopathic Normal Pressure Hydrocephalus: Patient Characteristics and Demographic Insights
Bibliographic record
Abstract
BACKGROUND: Idiopathic normal pressure hydrocephalus (iNPH) is characterized by gait disturbances, cognitive impairment and urinary dysfunction. Early diagnosis is essential to ensure timely shunt treatment. However, patient identification remains challenging due to limited studies, mostly from Asia and Europe, which restrict generalizability to other geographic areas. Moreover, demographic factors (age, sex, education) influence cognitive and gait performance in other neurological conditions, but their impact on iNPH remains unclear. This study aimed to characterize the demographic, vascular, cognitive and gait profiles of iNPH patients in Eastern Quebec (Canada) and determine how demographic factors influence performance outcomes. METHODS: A retrospective chart review was conducted on 175 patients diagnosed with probable iNPH at a specialized neurology center in Eastern Quebec. Demographic data, vascular risk factors and cognitive and gait outcomes were extracted from medical records. Descriptive statistics were used to characterize the sample, and multiple linear regressions assessed the effect of demographic factors on performance outcomes. RESULTS: The cohort had a mean age of 73.9 years and a mean education level of 11.9 years. Age and education significantly predicted over half of the cognitive test results, while age was the only significant predictor of gait. Hypertension (58%) and hyperlipidemia (47%) were more prevalent than diabetes (26%), differing from previous studies where diabetes was the second most reported vascular risk factor after hypertension. CONCLUSIONS: Clinical heterogeneity characterizes iNPH patients in Eastern Quebec. Differences in the prevalence of vascular risk factors compared to previous studies may reflect geographic variability in the clinical presentation of this condition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".