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Record W2167123485 · doi:10.1186/1472-6874-11-24

The role of female hormones on lung function in chronic lung diseases

2011· review· en· W2167123485 on OpenAlexafffund
Anthony Raymond Tam, Don Morrish, Samuel Wadsworth, Delbert R. Dorscheid, SF Paul Man, Don D. Sin

Bibliographic record

VenueBMC Women s Health · 2011
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of AlbertaUniversity of British ColumbiaProvidence Health Care
FundersCanadian Institutes of Health Research
KeywordsMedicineCOPDAsthmaLungContext (archaeology)PathogenesisAirwayHormoneImmunologyCystic fibrosisDiseaseMenstrual cyclePhysiologyInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The prevalence, morbidity, and mortality of inflammatory lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF) are increasing in women. There is a dearth of data on the biological mechanisms to explain such observations. However, some large epidemiologic studies suggest that lung function fluctuates during the menstrual cycle in female patients with airways disease but not in women without disease, suggesting that circulating estradiol and progesterone may be involved in this process. DISCUSSION: In asthma, estradiol shuttles adaptive immunity towards the TH2 phenotype while in smokers estrogens may be involved in the generation of toxic intermediate metabolites in the airways of female smokers, which may be relevant in COPD pathogenesis. In CF, estradiol has been demonstrated to up-regulate MUC5B gene in human airway epithelial cells and inhibit chloride secretion in the airways. Progesterone may augment airway inflammation. SUMMARY: Taken together, clinical and in-vivo data have demonstrated a sex-related difference in that females may be more susceptible to the pathogenesis of lung diseases. In this paper, we review the effect of female sex hormones in the context of these inflammatory airway diseases.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.980
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.385
Teacher spread0.343 · 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 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

Citations199
Published2011
Admission routes2
Has abstractyes

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