A comparative blind study between skin biopsy and seed amplification assay to disclose pathological α-synuclein in RBD
Bibliographic record
Abstract
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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".