Frequency, pattern and predictors of cognitive impairments in patients with Parkinson’s disease using the Community Screening Instrument for Dementia
Bibliographic record
Abstract
Background Parkinson’s disease (PD) is a chronic neurodegenerative disorder complicated by cognitive dysfunctions which are associated with increased caregiver burden, pressure on community health facilities, and mortality in affected patients. Most of the data concerning cognitive dysfunctions in PD are from studies conducted in Europe and North America, but there is paucity of data from Sub-Saharan Africa. Objective The objective of this study is to determine the frequency, pattern and predictors of cognitive impairments amongst patients with Parkinson’s disease. Materials and methods This was a cross sectional case control study carried out at a tertiary health facility in South-south Nigeria. Participants with PD were consecutively recruited from the neurology outpatient clinics. Demographic and disease-specific data were obtained with the use of a pre-tested questionnaire. Cognitive performance of thirty patients with PD were compared with thirty demographically matched controls using the Community Screening Instrument for Dementia (CSID). CSID was already validated among Nigerians. Results The frequency of cognitive impairment using the CSID was 50% for PD patients (3.3% for controls). Poor cognitive performance was observed across several cognitive domains including language, executive dysfunction, psychomotor speed, and constructional apraxia among PD patients. The independent predictors of the overall cognitive impairment in patients with PD determined by logistic regression analysis include recall deficiency (p = 0.007), impairment with naming (p = 0.044), apraxia (p = 0.003), Hoen&Yahr staging (p = 0.046), UPDRS score (p = 0.015) and age at presentation (p = 0.014). Conclusion Cognitive impairments occur more frequently in patients with PD compared to controls. This study also demonstrated the predictive role of severity of disease based on Hoehn &Yahr staging and UPDRS score, and presence of recall deficiency, poor naming ability and apraxia.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".