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Record W2547758161 · doi:10.25011/cim.v39i5.27148

Association between obstructive sleep apnea and metabolic syndrome: a meta-analysis

2016· review· en· W2547758161 on OpenAlexvenueno aff
Delei Kong, Zheng Qin, Wei Wang, Ying Pan, Jian Kang, Jian Pang

Bibliographic record

VenueClinical and investigative medicine · 2016
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
Fundersnot available
KeywordsObstructive sleep apneaMedicineMeta-analysisMetabolic syndromeInternal medicineBlood pressureSleep apneaCardiologyObesity

Abstract

fetched live from OpenAlex

Purpose Evidence suggests that obstructive sleep apnea (OSA) is related to metabolic syndrome; however, the relationship among metabolic syndrome parameters (blood pressure, fasting blood glucose (FBG), high density lipoprotein (HDL) and low density lipoprotein (LDL)) and OSA is unclear. Methods PRISMA guidelines were followed for this study. Medline, Cochrane, EMBASE and Google Scholar databases were searched until December 23, 2015, using following terms: obstructive sleep apnea, sleep apnea, OSA and metabolic syndrome. Results Ten studies were included in the analysis which included 2053 patients. Patients with OSA had higher systolic blood pressure (SBP) (pooled standard mean difference (SMD) = 0.56, 95% CI, 0.40 to 0.71, P<0.001), lower levels of HDL (pooled SMD = -0.27, 95% CI, -0.38 to -0.16, P<0.001), and higher levels LDL (pooled SMD = 0.26, 95% CI, 0.07 to 0.45, P=0.007) than patients without OSA. OSA was also found to be associated with increased triglyceride levels (pooled SMD = 0.26, 95%CI, 0.07 to 0.45, P=0.007) and higher FBG (pooled SMD = 0.35, 95%CI, 0.18 to 0.53, P<0.001). Conclusion This meta-analysis found that OSA was associated with abnormal levels of multiple parameters that are markers for metabolic syndrome, and suggests that OSA broadly affects this disease. Understanding the relationships between OSA and metabolic syndrome may allow the early identification of OSA patients who may develop diseases related to metabolic syndrome such as type 2 diabetes and cardiovascular disease.

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)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.054
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.310
GPT teacher head0.438
Teacher spread0.128 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations28
Published2016
Admission routes1
Has abstractyes

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