REM Sleep Behavior Disorder: How Useful is it for the Differential Diagnosis of Parkinsonism? (P7.306)
Bibliographic record
Abstract
OBJECTIVE: to analyze the presence of pRBD in a large sample of patients with neurodegenerative parkinsonism and a clinically defined diagnosis, and to estimate the utility and performance of this parasomnia as a determinant of the etiologic diagnosis. BACKGROUND: REM sleep behavior disorder (RBD) is typically linked to synucleinopathies (SP). In this study we analyzed the utility and performance of RBD as a tool for the differential diagnosis of the most common forms of degenerative parkinsonism, including SPs and tauopathies. DESIGN/METHODS: Patients with a syndromic diagnosis of degenerative parkinsonism matched for gender, age, and disease stage were assessed using a structured protocol with demographic and clinical data, including the diagnosis of probable RBD (pRBD), ascertained clinically using established criteria. RESULTS: One hundred cases of Parkinson’s disease (PD), 87 with progressive supranuclear palsy (PSP), 72 with the parkinsonian form of multiple system atrophy (MSA), 50 with dementia with Lewy bodies (DLB), and 18 with corticobasal degeneration (CBD) were included. pRBD was found in 58 (58[percnt]) of the PD patients, 59 (81.9[percnt]) of those with MSA, 37 (74[percnt]) with DLB, 32 (36.7[percnt]) with PSP, and one (5.5[percnt]) with CBD. Among the SPs, pRBD was significantly more common in MSA when compared with PD patients. Differences were also significant individually for all SPs when compared to PSP. The positive predictive value (PPV) of pRBD for a SP was 82.3[percnt], but sensitivity was 69.4[percnt] and specificity 68.6[percnt]. CONCLUSIONS: In our sample, pRBD was more frequent in SPs than in PSP and CBD, however, its’ frequency in PSP was significant. Although pRBD had a good PPV for a SP, all other measurements used for determine diagnostic performance were disappointing. Study Supported by:
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".