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Record W2415175064 · doi:10.1155/2016/7947198

Sleep in Chronic Obstructive Pulmonary Disease: Evidence Gaps and Challenges

2016· review· en· W2415175064 on OpenAlexaff
Rachel Jen, Yanru Li, Robert L. Owens, Atul Malhotra

Bibliographic record

VenueCanadian Respiratory Journal · 2016
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British Columbia
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineCOPDObstructive sleep apneaPulmonary diseaseEpidemiologyDiseaseIntensive care medicineSleep apneaPopulationSleep (system call)Overlap syndromeEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease (COPD) prevalence is rising to epidemic proportions due to historical smoking trends, the aging of the population, and air pollution. Although blaming the victims has been common in COPD, the majority of COPD worldwide is now thought to be nonsmoking related, that is, caused by air pollution and cookstove exposure. It is increasingly appreciated that subjective and objective sleep disturbances are common in COPD, although strong epidemiological data are lacking. People with obstructive sleep apnea (OSA) plus COPD (the so-called overlap syndrome) have a high risk of cardiovascular death, although again mechanisms are unknown and untested. This review aims to draw attention to the problem of sleep in COPD, to encourage clinicians to ask their patients about symptoms, and to stimulate further research in this area given the large burden of the 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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.086
GPT teacher head0.345
Teacher spread0.259 · 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.

Study designNot applicable
DomainMethods
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

Citations45
Published2016
Admission routes1
Has abstractyes

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