Sleep quality in patients with Parkinson’s disease in the Republic of Moldova – preliminary results
Bibliographic record
Abstract
Background and objectives. The rising prevalence of sleep issues are a consequent societal problem, notably impacting individuals with Parkinson’s disease (PD). This study aims to examine the differences in sleep quality between diagnosed patients and their counterparts, while also highlighting its effects on their symptoms and quality of life. Materials and methods. This study enrolled 37 PD subjects. Sleep quality, established via the Pittsburgh Sleep Quality Index (PSQI), was compared through a case control approach against 49 control subjects. PD symptomatology assessment was ensured via: Hoehn & Yahr (H&Y); Movement Disorder Society-Unified Parkinson’s disease rating scale (MDS-UPDRS I-IV); Non-motor Symptom Scale (NMSS); Beck Depression Inventory (BDI); Montreal Cognitive Assessment (MoCA); Scale for Outcomes in Parkinson’s disease – Psychosocial Functioning (SCOPA-PS); 39-Item Parkinson’s Disease Questionnaire (PDQ-39). Results. Compared to their homologues, PD subjects were prone to worse subjective sleep quality (18.9% vs. 8.2%), sleep efficiency (13.5% vs. 4.1%), diurnal functionality (27% vs. 12.2%), sleep related breathing disorders (62.2% vs. 46.9%). The global PSQI positively correlates to H&Y staging (rp= 0.443, p=0.008), UPDRS-III (rp= 0.369, p=0.029), UPDRS-IV (rp=0.412, p=0.011). PD subjects with PSQI >5 registered higher UPDRS-III (p=0.091), BDI (p=0.928), SCOPA-PS (p=0.051). PSQI5 correlates to PDQ-39 (rp=0.423, p=0.010) and SCOPA-PS (rp= 0.462, p=0.004). Conclusions. The study proved a clear correlation between altered sleep patterns and the clinical presentation of PD delineating the worsening of motor along to non-motor symptoms. In addition, the quality of life along to the psychosocial functioning of PD subjects is at risk in those manifesting sleep disturbances. Correspondingly, a greater interest should be applied in the establishment of prophylactic measures.
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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 | 0.001 |
| 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) | 0.001 | 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".