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Record W4323050059 · doi:10.1038/s41531-023-00473-5

A comparative blind study between skin biopsy and seed amplification assay to disclose pathological α-synuclein in RBD

2023· article· en· W4323050059 on OpenAlexfundno aff
Rocco Liguori, Vincenzo Donadio, Zerui Wang, Alex Incensi, Giovanni Rizzo, Elena Antelmi, Francesco Biscarini, Fabio Pizza, Wq Zou, Giuseppe Plazzi

Bibliographic record

Venuenpj Parkinson s Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of HealthU.S. Department of Health and Human ServicesCase Western Reserve UniversityNational Institute of Neurological Disorders and StrokeAlzheimer's AssociationMinistero della SaluteMichael J. Fox Foundation for Parkinson's ResearchWeston Brain Institute
KeywordsMedicinePathologicalSkin biopsyBiopsyPathologyCerebrospinal fluidREM sleep behavior disorderNeurologyNarcolepsyInternal medicineGastroenterologyParkinson's diseaseDisease

Abstract

fetched live from OpenAlex

To compare the diagnostic accuracy of the immunofluorescence (IF) technique and aSyn-seed amplification assay (aSyn-SAA) of skin and cerebrospinal fluid (CSF) in disclosing pathological α-syn in idiopathic idiopathic REM sleep behavior disorder (iRBD) as early phase of a synucleinopathy. We prospectively recruited 41 patients with iRBD and 40 matched clinical controls including RBD associated with type 1 Narcolepsy (RBD-NT1, 21 patients), iatrogenic causes (2 pt) or OSAS (6 pt) and 11 patients with peripheral neuropathies. IF from samples taken by skin biopsy and aSyn-SAA from skin and CSF samples were analysed blinded to the clinical diagnosis. IF showed a good diagnostic accuracy (89%) that was lower in the case of skin and CSF-based aSyn-SAA (70% and 69%, respectively) because of a lower sensitivity and specificity. However, IF showed a significant agreement with CSF aSyn-SAA. In conclusion, our data may favor the use of skin biopsy and aSyn-SAA as diagnostic tools for a synucleinopathy in iRBD.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.075
GPT teacher head0.355
Teacher spread0.280 · 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.

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

Citations52
Published2023
Admission routes1
Has abstractyes

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