Diagnosing REM sleep behavior disorder in Parkinson’s disease without a gold standard: a latent-class model study
Bibliographic record
Abstract
STUDY OBJECTIVES: To ascertain whether current diagnostic criteria for REM sleep behavior disorder (RBD) are appropriate in patients with Parkinson's disease (PD) consulting a movement disorder center, to evaluate the accuracy of REM sleep without atonia (RSWA) thresholds and determine the value of screening questionnaires to discriminate PD patients with RBD. METHODS: One hundred twenty-eight consecutive PD patients (M = 80; mean age: 65.6 ± 8.3 years) underwent screening questionnaires, followed by a sleep-focused interview and a full-night video-polysomnography (vPSG). Without a gold standard, latent class models (LCMs) were applied to create an unobserved ("latent") variable. Sensitivity analysis was performed using RSWA cutoff derived from two visual scoring methods. Finally, we assessed the respective diagnostic performance of each diagnostic criterion for RBD and of the screening questionnaires. RESULTS: According to the best LCM-derived model, patients having either "history" or "video" with RSWA or alternatively showing both "history" and "video" without RSWA were classified as having RBD. Using both SINBAR and Montreal scoring methods, RSWA criterion showed the highest sensitivity while concomitant history of RBD and vPSG-documented behaviors, regardless to presence of RSWA, displayed the highest specificity. Currently recommended diagnostic threshold of RSWA was found to be optimal in our large cohort of PD patients. Both the RBD screening questionnaire (RBDSQ) and the RBD single question (RBD1Q) showed poor sensitivity and specificity. CONCLUSIONS: Results of the best LCM for diagnosis of RBD in PD were consistent with the current diagnostic criteria. Moreover, RBD might be considered in those PD patients with both history and vPSG-documented dream enactment behaviors, but with RSWA values within the normal range.
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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 | 0.033 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".