The non-motor symptoms of Parkinson's disease of different motor types in early stage.
Bibliographic record
Abstract
OBJECTIVE: We aimed to compare the different non-motor symptoms of different motor phenotype Parkinson's disease (PD) at an early stage. PATIENTS AND METHODS: From January 2013 to November 2016, 120 cases of PD patients who were hospitalized in Neurology Department of the First Hospital of Huai'an in Jiangsu Province and 120 cases of healthy controls with matched age and gender, were included into the research. PD patients were administered with Non-Motor symptom questionnaire (NMSQuest), the Unified Parkinson's Disease Rating Scale III (UPDRS-III), the Mini-Mental State Examination score (MMSE), the Hoehn-Yahr classification, the MoCA, and GDS-15. The relationship between NMS burden and PD subtypes, age, gender and disease severity were examined using linear regression models. The prevalence of each NMS among different PD motor subtypes was analyzed using x2 test. RESULTS: Compared with the healthy controls, PD patients had a higher number of NMS. The prevalence of NMS in postural instability gait difficulty (PIGD) group is higher than that in tremor dominant (TD) group. There is no significant correlation between age, gender, MMSE scores, MoCA scores and the number of NMS. PD patients with higher UPDRS-III scores and a longer course of disease had a higher prevalence of NMS. CONCLUSIONS: NMS is also common in PD patients at an early stage. The PIGD group who have more axial injuries and more severe motor symptoms, have a higher risk of NMS burden than PD patients in TD group.
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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 | 0.001 | 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".