1040 Scoring Large Muscle Movements in Pediatric Sleep Studies: An Educational Module for Sleep Technologists
Bibliographic record
Abstract
Abstract Introduction Restless Sleep Disorder (RSD) has recently been recognized as a distinct sleep disorder, with Large Muscle Movement (LMM) scoring being a crucial component of its diagnostic criteria. This project aimed to develop and implement an online educational module to enhance the knowledge and skills of sleep technologists to accurately score LMMs. Methods We constructed an online learning module that included training on the identification and scoring of LMMs in pediatric sleep studies. The module consisted of a 10-question pre-assessment, the educational content (which explained the background of RSD diagnosis, the role of LMMs in diagnostic criteria, and current scoring rules), a repeat of the 10 questions for post-assessment, and a feedback section regarding the module and LMM scoring in general. Results Preliminary results indicate a significant improvement in the accuracy and consistency of LMM scoring among sleep technologists who completed the module. The average post-module assessment scores showed a marked increase compared to pre-module scores, with total scores on the 10-question learning assessment increasing from 63.3+/-20.6% pre-module to 90.0+/- 8.9% post-module (p=0.017). Eighty percent of technologists agreed or strongly agreed with the statement that “As a result of taking this course I am confident I can recognize the clinical characteristics, symptoms, and diagnostic criteria of RSD.” Similarly, 60% agreed or strongly agreed with the state that “As a result of taking this course, I am confident I can accurately apply scoring guidelines for LMM events in sleep studies.” Feedback from technologists regarding the module itself, as well as questions and suggestions regarding LMM scoring in general, provided valuable information for potential future revisions of scoring rules. Conclusion An online educational module significantly improved the knowledge and ability of sleep technologists to score LMMs accurately, thereby aiding in the diagnosis of RSD. This initiative highlights the importance of continuous education and training in maintaining high standards of care in pediatric sleep studies. Support (if any) None.
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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 | 0.000 |
| 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".