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Record W4410482085 · doi:10.1093/sleep/zsaf090.1040

1040 Scoring Large Muscle Movements in Pediatric Sleep Studies: An Educational Module for Sleep Technologists

2025· article· en· W4410482085 on OpenAlexfundno aff
Shauna Michelle Vandoren, David G. Ingram

Bibliographic record

VenueSLEEP · 2025
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersBC Children's Hospital
KeywordsSleep (system call)Physical medicine and rehabilitationMedicinePhysical therapyPsychologyComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.334
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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