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Record W2087462329 · doi:10.1513/pats.200707-091sd

Reflecting the Changing Face of Chronic Obstructive Pulmonary Disease: Sex and Gender in Public Education Materials on COPD

2007· review· en· W2087462329 on OpenAlexaff
Ann Pederson, Kristy A. K. Hoyak, Pat G. Camp

Bibliographic record

VenueProceedings of the American Thoracic Society · 2007
Typereview
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of British ColumbiaBritish Columbia Centre of Excellence for Women's Health
Fundersnot available
KeywordsMedicineCOPDPulmonary diseaseFace (sociological concept)GerontologyIntensive care medicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Emerging evidence suggests that sex and gender differences exist in the prevalence, susceptibility to, severity of, and response to treatment and management of, chronic obstructive pulmonary disease (COPD). However, the identification of knowledge gaps regarding sex, gender, and COPD involves not only pinpointing what areas of etiology, epidemiology, and management need to be examined from a sex and gender perspective (as discussed in other articles of this issue), but also must include discussion of how such new and emerging findings are translated to health care professionals, policy makers, and the general population. How emerging knowledge is reflected in educational, awareness-raising, and policy materials made available to the public through community-based organizations, lung health advocacy organizations, the government, and clinicians is not known. A preliminary examination of such documents from around the world suggests that many materials continue to present COPD as a disease that primarily afflicts men. This gap in the translation of research knowledge may be specifically problematic for women-for example, because they may not be adequately informed of the symptoms of COPD, be appropriately screened for the disease, or receive appropriate interventions and treatment.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.184
GPT teacher head0.521
Teacher spread0.337 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations12
Published2007
Admission routes1
Has abstractyes

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