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Record W7132966958

The Effects of Sleep-Related Breathing Disorders on Heart Rate Variability and Cardiac Arrhythmias During Sleep in Individuals Living with Spinal Cord Injury

2025· dissertation· W7132966958 on OpenAlexaff
Julia Ines Coschignano

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsHeart rate variabilitySpinal cord injuryHeart rateBreathingSleep (system call)Prospective cohort studyElectrocardiographyAutonomic nervous systemSleep apnea
DOInot available

Abstract

fetched live from OpenAlex

Sleep-related breathing disorders (SRBD) and cardiovascular dysfunction are common secondarymedical conditions that typically alter sympathetic activity after spinal cord injury (SCI). This prospective cross-sectional study tested the clinically relevant hypothesis on whether moderate- to-severe SRBD is associated with decreased heart rate variability (HRV) and increased cardiac arrhythmias compared with mild/no SRBD in individuals with cervical/high-thoracic SCI. This study included adults living with subacute/chronic, cervical/high thoracic (at T6 or more cranial) SCI, who were not previously screened for SRBD. All participants underwent a continuous electrocardiogram paired with an unattended sleep screening test that was used to quantify the degree of SRBD. Overall, our results suggest that individuals with cervical/high-thoracic SCI who develop moderate-to-severe SRBD have decreased nocturnal HRV along with reduced PNS activity, but similar SNS activity regardless of degree of SRBD. This suggests a less adaptive cardiovascular system in individuals living with SCI and moderate-to-severe SRBDs, implicating a higher risk for cardiovascular adverse events/diseases.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.311
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), 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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