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Record W2891509957 · doi:10.1159/000492329

In a Nutshell: Findings from the Bruneck Study

2018· review· en· W2891509957 on OpenAlexaboutno aff
Stefan Kiechl, Johann Willeit

Bibliographic record

VenueGerontology · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyGerontologyDiseaseMedicineLongevityQuarter (Canadian coin)DemographyEnvironmental healthPathologyGeography

Abstract

fetched live from OpenAlex

The Bruneck Study is a prospective community-based study enrolling an age- and sex-stratified random sample of 1,000 men and women. It achieved an extraordinary participation rate of 93.4% and a near-complete long-term follow-up exceeding a quarter of a century (1990-2018). High-quality ascertainment of most common human diseases enables reliable evaluation of: disease epidemiology, overlaps between age-related diseases, and risk factors, and discovery of novel biomarkers. Research priorities include atherosclerosis, cardiovascular disease, aging and longevity, neurological diseases, disorders of the bone, and cancer. This review summarizes the main scientific contributions of the Bruneck Study over the past decades, outlines recent highlights, and gives an outlook of what is planned next.

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)0.005
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
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 head0.088
GPT teacher head0.368
Teacher spread0.280 · 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
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

Citations24
Published2018
Admission routes1
Has abstractyes

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