Polysomnographic markers of REM sleep behavior disorder in Parkinson’s Disease: methodological issues, diagnostic accuracy and progression over time
Bibliographic record
Abstract
Up to 60% of Parkinson’s disease (PD) patients have REM sleep behavior disorder (RBD), a parasomnia characterized by a loss of REM sleep muscle atonia and dream-enacting behaviors, usually associated to vivid dreams. REM sleep without atonia (RSWA), characterized by a sustained tonic and/or phasic muscle activity during REM sleep, is the polysomnographic hallmark of RBD. PD patients with RBD (PDRBD+) are more severely impaired in both motor and non-motor domains, compared to those without RBD, and they have an increased risk of dementia. Thus, RBD may be a biomarker of more widespread/malignant phenotype and correct identification of RBD in PD may bear clinical, therapeutic, and prognostic implications. However, RBD diagnostic criteria have been defined and screening tools have been developed mainly based on idiopathic RBD population. Moreover, little is known about the evolution of both clinical and video-polysomnographic (vPSG) measures of RBD in relationships with the progression of motor and non-motor symptoms of PD. Actually, RBD may precede, concurs or follow the onset of PD by many years, but an improvement of RBD symptoms is also occasionally reported in PD patients over time. Longitudinal assessment of RBD performed by questionnaire in PD population has led to controversial results and, so far, only one vPSG study has been performed in patients with PDRBD+. In this thesis, we first aimed to assess the concordance of two visual scoring method for RSWA, namely the Montreal and the SINBAR, and to compare the two methods with an automated scoring method, in a large cohort of patients with PD consecutively seen at Movement Disorder Centers. Then, in a second study, we aimed to ascertain whether current diagnostic criteria for RBD, mainly developed based on idiopathic RBD, are appropriate to diagnose RBD in PD patients and to assess the sensitivity and specificity of the two most used RBD screening questionnaires, namely the RBDSQ and the RBD1Q. Finally, in the third study, we sought to longitudinally evaluate clinical and neurophysiological features of RBD at the end of a 3-years follow-up including RSWA, and to assess the relationship between the evolution of RSWA and the progression of symptoms in a large cohort of PD patients with RBD, in order to ascertain whether RBD represents a stable marker in PD. Assessing the appropriateness of screening and diagnostic criteria and elucidating the time course of RBD in PD would be crucial to determine the usefulness of this marker in view of future neuroprotective and disease modifying trials.
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 | 0.048 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".