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Record W2775069330 · doi:10.1080/19485565.2017.1403304

“Inflammaging” and Estradiol among Older U.S. Women: A Nationally Representative Longitudinal Study

2017· article· en· W2775069330 on OpenAlexaff
Aniruddha Das

Bibliographic record

VenueBiodemography and Social Biology · 2017
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsMcGill University
Fundersnot available
KeywordsLongitudinal studyGerontologySenescenceDemographyWindow of opportunityPopulationAgeingLongitudinal dataHealth and Retirement StudyMedicinePsychologyInternal medicineSociology

Abstract

fetched live from OpenAlex

Despite accumulating small-sample and clinical evidence on "inflammaging," no population-representative longitudinal studies have specifically examined women's late-life inflammation trends. While a range of studies indicates estradiol's immunomodulation role, evidence is contradictory on whether its effects are pro- or antiinflammatory among older women. Using longitudinal data from the first two waves of the National Social Life, Health and Aging Project-a national probability sample of older U.S. adults aged 57 to 85 years at baseline-this study began to fill these gaps. Findings suggested rather than being a lifelong process, older women's inflammaging may have a biological window that closes with senescence. Moreover, their endogenous estradiol plays a proinflammatory rather than immunoprotective role. Nor does this sex steroid modulate age effects on women's inflammation. More sex-specific basic research is needed on causal mechanisms underlying women's late-life inflammaging patterns.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.376
Teacher spread0.314 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2017
Admission routes1
Has abstractyes

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