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Record W2895011371 · doi:10.1097/mcp.0000000000000525

Obstructive sleep apnea and chronic kidney disease

2018· review· en· W2895011371 on OpenAlexaff
Chou-Han Lin, Elisa Perger, Owen D. Lyons

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsWomen's College HospitalToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsMedicineObstructive sleep apneaKidney diseasePathogenesisSleep apneaHypoxia (environmental)Internal medicineRenal functionDiseaseCardiologyRenal replacement therapyKidneyIntensive care medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Obstructive sleep apnea (OSA) is highly prevalent in patients with chronic kidney disease (CKD). The relationship between OSA and CKD is likely to be bi-directional. On one hand, the presence of OSA leads to intermittent hypoxia, sympathetic nervous system activity, and hypertension, all of which may have deleterious effects on kidney function. On the other hand, in patients with end-stage renal disease (ESRD), intensification of renal replacement therapy has been shown to attenuate sleep apnea severity, suggesting that the renal disease itself contributes to the pathogenesis of OSA. The present review describes our current understanding of the bi-directional relationship between OSA and CKD. RECENT FINDINGS: Studies suggest that the presence of OSA and nocturnal hypoxia may lead to worsening of kidney function. One potential mechanism is activation of the renin-angiotensin system by OSA, an effect which may be attenuated by CPAP therapy. In ESRD, fluid overload plays an important role in the pathogenesis of OSA and fluid removal by ultrafiltration leads to marked improvements in sleep apnea severity. SUMMARY: OSA is associated with accelerated loss of kidney function. In patients with ESRD, fluid overload plays an important role in the pathogenesis of OSA.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.087
GPT teacher head0.409
Teacher spread0.322 · 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.

Study designNot applicable
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

Citations55
Published2018
Admission routes1
Has abstractyes

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