Alexithymia Predicts Cognitive Deficits In Patients With Idiopathic Parkinson's Disease.
Bibliographic record
Abstract
BACKGROUND: In recent years, alexithymia has gained attention of medical researchers as a prognostic factor for serious health problems. The present study aimed to assess alexithymia as a determinant of cognitive decline in patients with Parkinson's disease.. AIM: The aim of our study is to assess the pain relief after CPN, reduction in analgesics consumption and evaluation of patient satisfaction post procedure. METHODS: Patients diagnosed with Parkinson's disease (n=60) at Bahawal Victoria Hospital, Civil Hospital Bahawalpur and Nishter Hospital Multan during May 2016 until June 2017 participated in the study. Healthy individuals (n=60) took part in the study from local community as controls. Participants completed Bermond-Vorst Alexithymia Questionnaire and Montreal Cognitive Assessment. It was a cross sectional study design. Purposive sampling technique was used and data was analysed through multivariate analysis of variance and bivariate correlation. RESULTS: Patients with Parkinson's disease (177.96±8.93) showed higher attitudes of alexithymia as compared with healthy individuals (37.46±8.01), F (1,118) = 8216.52, p<0.001, ηp2=.98. In contrast with healthy controls (28.60±0.58), patients with Parkinson's disease (2.25±.95) were cognitively impaired F (1,118) =36424.38, p<0.001, ηp2=.99. Alexithymia was a significant predictor of cognitive performance (R2=0.99, F (2, 119) = 5698.95, p<0.001). CONCLUSIONS: Alexithymia is a significant marker of cognitive decline in patients with Parkinson's disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".