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Record W2547522166 · doi:10.1016/j.chest.2016.10.031

Improving the Management of COPD in Women

2016· review· en· W2547522166 on OpenAlexafffund
Christine Jenkins, Kenneth R. Chapman, James F. Donohue, Nicolás Roche, Ioanna Tsiligianni, MeiLan K. Han

Bibliographic record

VenueCHEST Journal · 2016
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsToronto Western HospitalUniversity Health Network
FundersCanadian Institutes of Health ResearchNovartis PharmaGrifolsAstraZenecaCSL BehringTeva Pharmaceutical IndustriesSanofiAmgenPfizerMylanUniversity Health NetworkGlaxoSmithKline
KeywordsCOPDMedicineDiseaseAffect (linguistics)GerontologyIntensive care medicinePsychiatryPathologyPsychology

Abstract

fetched live from OpenAlex

COPD is a highly debilitating disease that represents a substantial and growing health burden in women. There is increasing evidence for sex-related differences in COPD risk, progression, and outcomes. However, the disease receives scant attention as a women's health issue. Thus, a multifaceted approach is required to address COPD in women, including greater awareness, minimization of risk, and further elucidation of the sex-specific factors (biological and cultural) that affect risk, disease progression, and treatment success. This article reviews the current literature on the topic and provides suggestions for achieving better outcomes for the millions of women with COPD worldwide.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.039
GPT teacher head0.344
Teacher spread0.305 · 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 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

Citations113
Published2016
Admission routes2
Has abstractyes

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