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Record W2931830864 · doi:10.1016/j.smrv.2019.03.010

Matrix metalloproteinases as possible biomarkers of obstructive sleep apnea severity – A systematic review

2019· review· en· W2931830864 on OpenAlexafffund
Aleksandra Franczak, Iwona Bil‐Lula, Grzegorz Sawicki, Mark Fenton, Najib Ayas, Robert Skomro

Bibliographic record

VenueSleep Medicine Reviews · 2019
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British ColumbiaCanadian Sleep & Circadian NetworkUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsObstructive sleep apneaMedicineMatrix metalloproteinaseHypoxemiaOxidative stressSleep apneaHypoxia (environmental)Intermittent hypoxiaHypopneaInternal medicineCardiologyApneaBioinformaticsPolysomnographyBiology

Abstract

fetched live from OpenAlex

Obstructive sleep apnea is an underdiagnosed sleep-related breathing disorder affecting millions of people. Recurrent episodes of apnea/hypopnea result in intermittent hypoxia leading to oxidative stress. Obstructive sleep apnea is considered an independent risk factor for cardiovascular disease but the exact pathophysiology of adverse cardiovascular outcomes of obstructive sleep apnea has not been fully elucidated. Matrix metalloproteinases (MMPs) have been associated with both oxidative stress and cardiovascular diseases. Hypoxic conditions were shown to influence MMP expression, secretion and activity. Moreover, matrix metalloproteinases contribute to ischemia/reperfusion injury. Therefore, action of matrix metalloproteinases can provide a possible molecular mechanism linking obstructive sleep apnea with oxidative stress and cardiovascular disease. The aim of this paper was to review the current evidence of association between matrix metalloproteinases and obstructive sleep apnea with focus on hypoxemia and severity of obstructive sleep apnea.

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)0.007
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.344
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0290.004
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.003

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 head0.063
GPT teacher head0.396
Teacher spread0.333 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
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

Citations36
Published2019
Admission routes2
Has abstractyes

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