Staging Parkinson's disease according to the MNCD classification correlates with caregiver burden
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Recently, we demonstrated that staging Parkinson's disease (PD) with a novel simple classification called MNCD, based on four axes (motor, non-motor, cognition, and dependency) and five stages, correlated with disease severity and patients' quality of life. Here, we analyzed the correlation of MNCD staging with PD caregiver's status. PATIENTS AND METHODS: Data from the baseline visit of PD patients and their principal caregiver recruited from 35 centers in Spain from the COPPADIS cohort from January 2016 to November 2017 were used to apply the MNCD total score (from 0 to 12) and MNCD stages (from 1 to 5) in this cross-sectional analysis. Caregivers completed the Zarit Caregiver Burden Inventory (ZCBI), Caregiver Strain Index (CSI), Beck Depression Inventory-II (BDI-II), PQ-10, and EUROHIS-QOL 8-item index (EUROHIS-QOL8). RESULTS: Two hundred and twenty-four PD patients (63 ± 9.6 years old; 61.2% males) and their caregivers (58.5 ± 12.1 years old; 67.9% females) were included. The frequency of MNCD stages was 1, 7.6%; 2, 58.9%; 3, 31.3%; and 4-5, 2.2%. A more advanced MNCD stage was associated with a higher score on the ZCBI (p < .0001) and CSI (p < .0001), and a lower score on the PQ-10 (p = .001), but no significant differences were observed in the BDI-II (p = .310) and EUROHIS-QOL8 (p = .133). Moderate correlations were observed between the MNCD total score and the ZCBI (r = .496; p < .0001), CSI (r = .433; p < .0001), and BDI-II (r = .306; p < .0001) in caregivers. CONCLUSION: Staging PD according to the MNCD classification is correlated with caregivers' strain and burden.
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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 | 0.004 |
| 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 | 0.001 |
| 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".