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Record W2899967048 · doi:10.1051/medsci/201834f112

Effects of continuous positive airway pressure on elderly patients with obstructive sleep apnea: a meta-analysis

2018· review· en· W2899967048 on OpenAlexaboutno aff
Yongmei Jin, Yi Hu, Shu Li

Bibliographic record

Venuemédecine/sciences · 2018
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineContinuous positive airway pressureObstructive sleep apneaNeurocognitiveMeta-analysisSleep apneaClinical trialApneaInternal medicineQuality of life (healthcare)AirwayAnesthesiaCardiologyCognition

Abstract

fetched live from OpenAlex

OBJECTIVE: the aim of the study was to evaluate the efficacy of continuous positive airway pressure (CPAP) for the treatment of obstructive sleep apnea in the elderly. METHOD: a comprehensive search for qualified clinical trials was performed on April, 2016. Basic demographic information of enrolled subjects, study design, survival rate, cardiovascular events, quality of life scores, and neurocognitive data were extracted for analysis. RESULTS: A total of seven clinical trials were included in this meta-analysis, in which untreated elderly patients exhibited worse survival rate than those with CPAP (OR=2.22, 95% CI=1.64 to 3.01, P< 0.00001). Treated elderly patients exhibited less cardiovascular risk than those without CPAP (RR=0.49, 95% CI=0.36 to 0.66, P<0.00001) and a statistically significant improvement on all the domains of Quebec Sleepiness Questionnaire, supported by pooled weighted mean difference. Furthermore, CPAP treatment partially improved the cognitive functions. CONCLUSION: CPAP treatment achieves improvements in decreasing mortality and controlling cardiovascular events and exhibits few effects on neurocognitive function. Further large-scale, well-designed interventional investigation is needed.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.012
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.022
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.335
Teacher spread0.297 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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