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Record W1997389210 · doi:10.1016/j.exger.2013.12.004

Gender and telomere length: Systematic review and meta-analysis

2013· review· en· W1997389210 on OpenAlexaff
Michael P. Gardner, David Bann, Laura Wiley, Rachel Cooper, Rebecca Hardy, Dorothea Nitsch, Carmen Martín-Ruiz, Paul G. Shiels, Avan Aihie Sayer, Michelangela Barbieri, Sofie Bekaert, Claus Bischoff, Angela Brooks‐Wilson, Wei Chen, Cyrus Cooper, Kaare Christensen, Tim De Meyer, Ian J. Deary, Geoff Der, Ana Diez Roux, Annette L. Fitzpatrick, Anjum Hajat, Julius Halaschek-Wiener, Sarah E. Harris, Steven C. Hunt, Carol Jagger, Hyo-Sung Jeon, Robert C. Kaplan, Masayuki Kimura, Peter M. Lansdorp, Changyong Li, Toyoki Maeda, Massimo Mangino, Tim S. Nawrot, Peter M. Nilsson, Katarina Nordfjäll, Giuseppe Paolisso, Fu Ren, Karl Riabowol, Tony Robertson, Göran Roos, Jan A. Staessen, Tim D. Spector, Nelson L.S. Tang, Brad M. Unryn, Pim van der Harst, Jean Woo, Chao Xing, Mohammad E Yadegarfar, Jae Yong Park, Neal S. Young, Diana Kuh, Thomas von Zglinicki, Yoav Ben‐Shlomo

Bibliographic record

VenueExperimental Gerontology · 2013
Typereview
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of CalgaryInstitute of AgingUniversity of British ColumbiaBC Cancer Agency
FundersNational Heart, Lung, and Blood InstituteNational Institute on AgingEconomic and Social Research CouncilBiotechnology and Biological Sciences Research CouncilKidney Research UKNational Center for Research ResourcesNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institute for Health and Care ResearchWellcome Trust
KeywordsMeta-analysisTelomereGeneticsBiologyPsychologyMedicineInternal medicineDNA

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.018
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.254
GPT teacher head0.423
Teacher spread0.169 · 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 designMeta-analysis
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

Citations519
Published2013
Admission routes1
Has abstractno

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