Evidence map›Paper›PMID 38622100›Full record

ReviewBiochemistry. Biokhimiia2024

Exploring Patterns of Human Mortality and Aging: A Reliability Theory Viewpoint.

Leonid A Gavrilov, Natalia S Gavrilova

Open access · greenAbstract readReview
In one paragraph

Review in Biochemistry. Biokhimiia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Leonid A GavrilovNORC at the University of Chicago, Chicago, IL 60637, USA. lagavril@yahoo.com.
Natalia S GavrilovaNORC at the University of Chicago, Chicago, IL 60637, USA.
University of Chicago · US

Funding

Biodemography of Old-Age MortalityR21AG054849 · NIA · NATIONAL OPINION RESEARCH CENTER · PI GAVRILOVA, NATALIA S · 2017 to 2018
$428k
NIA NIH HHS R21 AG054849
6 · The paper itself

Abstract

The most important manifestation of aging is an increased risk of death with advancing age, a mortality pattern characterized by empirical regularities known as mortality laws. We highlight three significant ones: the Gompertz law, compensation effect of mortality (CEM), and late-life mortality deceleration and describe new developments in this area. It is predicted that CEM should result in declining relative variability of mortality at older ages. The quiescent phase hypothesis of negligible actuarial aging at younger adult ages is tested and refuted by analyzing mortality of the most recent birth cohorts. To comprehend the aging mechanisms, it is crucial to explain the observed empirical mortality patterns. As an illustrative example of data-directed modeling and the insights it provides, we briefly describe two different reliability models applied to human mortality patterns. The explanation of aging using a reliability theory approach aligns with evolutionary theories of aging, including idea of chronic phenoptosis. This alignment stems from their focus on elucidating the process of organismal deterioration itself, rather than addressing the reasons why organisms are not designed for perpetual existence. This article is a part of a special issue of the journal that commemorates the legacy of the eminent Russian scientist Vladimir Petrovich Skulachev (1935-2023) and his bold ideas about evolution of biological aging and phenoptosis.

Indexed as

AgingLongevityAdultCell DivisionHumansMortalityReproducibility of Resultsagingcompensation effect of mortalityevolutionary models of agingGompertz modelmortalitymortality decelerationreliability theory of aging

Identifiers

PMID38622100
PMCPMC11090256
OpenAlexW4392990597

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.