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Record W4404002829 · doi:10.1101/2024.10.31.24316398

The Relationship Between Biological Aging with Interdisciplinary Health Indicators: A Scoping Review

2024· review· en· W4404002829 on OpenAlexaff
Jennifer Reeves, Emma Knock, Zoë M. Gilson, Leah Derry, Miki C. McGhee, Katherine Taylor-Hood, Alison Ziesel, Hosna Jabbari, Lorelei Newton, Jo Ann Miller, Jie Zhang, Ryan E. Rhodes, Αναστασία Μαλλίδου, Theone Paterson

Bibliographic record

VenuemedRxiv · 2024
Typereview
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsHealth Sciences CentreUniversity of AlbertaUniversity of Victoria
Fundersnot available
KeywordsPsychologyGerontologyMedicine

Abstract

fetched live from OpenAlex

Abstract Objectives This review aims to provide a comprehensive examination of biomarkers and interdisciplinary variables related to aging. Methods This scoping review included studies which involved adult participants, and which reported on the relationship between any biomarker or biological age with chronological age. Results After screening, 447 articles met the selection criteria. Results were categorized into 10 distinct categories through an iterative process. Conclusions This review contributes information regarding the interdisciplinary influences on the rate of aging. Telomere length was the most commonly examined biomarker, and Horvath’s 353-CpG Pan-Tissue clock was the most common clock, with both demonstrating a strong and consistent relationship with chronological age. The interdisciplinary variables demonstrated relationships with biological aging with varying strengths and consistencies.

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)0.012
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0180.016
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.325
GPT teacher head0.559
Teacher spread0.234 · 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 designSystematic review
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

Citations1
Published2024
Admission routes1
Has abstractyes

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