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Record W4309509749 · doi:10.1002/brb3.2827

Clinical outcomes and cognitive impairments between progressive supranuclear palsy and multiple system atrophy

2022· article· en· W4309509749 on OpenAlexaboutno aff
Peifei Jia, Jinhong Zhang, Jiuyan Han, Yong Ji

Bibliographic record

VenueBrain and Behavior · 2022
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsProgressive supranuclear palsyMontreal Cognitive AssessmentParkinsonismMedicineAtrophyInternal medicineCognitionIncidence (geometry)Mini–Mental State ExaminationCognitive declineDyskinesiaConstipationParkinson's diseasePediatricsCognitive impairmentDementiaDiseasePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Both progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) belong to atypical parkinsonian syndromes. It is important to differentiate these diseases accurately. We compared clinical outcomes and cognitive impairments between PSP and MSA. METHODS: Eighty-five MSA parkinsonism type (MSA-P) patients and 76 PSP patients participated in this research. The Montreal Cognitive Assessment (MoCA) and the mini-mental state examination (MMSE) evaluated cognitive function. RESULTS: MSA-P patients had a significantly higher incidence of dyskinesia, fall, urinary symptoms, and constipation, whereas patients with PSP had a higher incidence of tremor and salivation. MSA-P patients had higher MMSE and MoCA scores than PSP patients. The MMSE score showed a diagnostic cut-off score of 24.5 in PSP versus MSA-P. The MoCA score showed a diagnostic cut-off score of 20.5 in PSP versus MSA-P. CONCLUSION: In conclusion, patients with PSP had differences in the clinical outcomes and cognitive impairments compared with MSA-P patients. PSP patients had more severe cognitive deficits. The score of MMSE and MoCA could be used in distinguishing MSA-P from PSP.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.339
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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