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Record W4308426661 · doi:10.1016/s2666-7568(22)00200-8

WHO working definition of vitality capacity for healthy longevity monitoring

2022· review· en· W4308426661 on OpenAlexaff
Ivan Bautmans, Veerle Knoop, Jotheeswaran Amuthavalli Thiyagarajan, Andrea B. Maier, John Beard, Ellen Freiberger, Daniel W. Belsky, Mylène Aubertin‐Leheudre, Christopher Mikton, Matteo Cesari, Yuka Sumi, Theresa Diaz, Anshu Banerjee

Bibliographic record

VenueThe Lancet Healthy Longevity · 2022
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité du Québec à Montréal
FundersVrije Universiteit BrusselWorld Health Organization
KeywordsVitalityLongevityMental capacityFunction (biology)PsychologyGerontologyCognitive psychologyMedicineBiology

Abstract

fetched live from OpenAlex

Intrinsic capacity, a crucial concept in healthy ageing, is defined by WHO as "the composite of all the physical and mental capacities that an individual can draw on at any point in time". Vitality capacity is considered the underlying physiological determinant of intrinsic capacity. To advance the measurement and monitoring of vitality capacity, a working group of WHO staff members and twenty experts representing six WHO regions was convened to discuss and clarify the attributes of vitality capacity and to develop a clear working definition of the concept. Potential biomarkers to measure vitality capacity were identified, and the following consensual working definition was developed: vitality capacity is a physiological state (due to normal or accelerated biological ageing processes) resulting from the interaction between multiple physiological systems, reflected in (the level of) energy and metabolism, neuromuscular function, and immune and stress response functions of the body.

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)0.010
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.005
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0040.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.004

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 head0.400
GPT teacher head0.428
Teacher spread0.028 · 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 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

Citations206
Published2022
Admission routes1
Has abstractyes

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