P005 Comparison of auto-detected REM sleep without Atonia before- and after-editing
Bibliographic record
Abstract
Abstract Introduction This study evaluates the impact of editing on auto-detection of REM sleep without atonia (RSWA) in three cohorts with different expected frequencies of abnormal RSWA. Methods Participants underwent in-home Sleep Profiler recordings with chin and arm EMG. Twenty-seven were diagnosed with REM sleep behavior disorder (RBD: women = 26%, age = 67+/-9.7), 45 with mild cognitive impartment (MCI = 39) or Alzheimer’s disease (AD = 6) (MCI/AD: women = 56%, age = 72+/-8.5) and 27 controls (CG: women = 70%, age = 64+/-11.9). Recordings were auto-scored for stage REM and phasic- or tonic-RSWA (“Auto”) using previously described procedures and then visually edited (“Edited”). Abnormal RSWA densities were assigned based on previously determined auto-detection thresholds for the Chin (>15%), Arm (>12.5%) and chin and/or arm (“Any” > 25%). Results In the RBD group, 18/22 (82%) participants were identified with abnormal Auto Chin-RSWA vs. Edited = 21/26 (81%), while 20/24(83%) had abnormal Auto Arm-RSWA vs. Edited = 24/27 (89%), and 18/24 (75%) had abnormal Any-RSWA vs. Edited = 23/27 (85%). For the MCI/AD cohort, 9/45 (20%) had abnormal Auto Chin-RSWA vs. edited = 7/45 (16%), while 4/45 had abnormal auto Arm-RSWA (9%) vs. Edited = 2/45 (4%), and 6/45 (13%) had Auto and Edited abnormal Any-RSWA. For the control group, Auto and Edited Chin-RSWA densities were abnormal in 3/27 (11%) and 4/27 (15%), while 4/27 (15%) had abnormal Auto Arm-RSWA vs. Edited = 3/27 (11%), and abnormal Auto Any-RSWA was observed in 2/27 (7%) vs. Edited = 3/27 (11%). Conclusions The accuracy of the auto-scoring suggests a foundational base that could help limit between-site variability during large-scale RBD screening in future multicenter clinical trials and population-based research studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".