MétaCan
Menu
Back to cohort
Record W2617024599 · doi:10.1093/gerona/glx009

A Comparison of Two Mouse Frailty Assessment Tools

2017· article· en· W2617024599 on OpenAlexaff
Alice E. Kane, Aniko Huizer‐Pajkos, John Mach, Sarah J. Mitchell, Rafael de Cabo, David G. Le Couteur, Susan E. Howlett, Sarah N. Hilmer

Bibliographic record

VenueThe Journals of Gerontology Series A · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFrailty IndexCreatinineAlanine aminotransferaseMedicineInternal medicineAlanine transaminaseGerontology

Abstract

fetched live from OpenAlex

The mouse clinical frailty index and the mouse frailty phenotype assessment are two recently developed tools used to assess frailty in mice. The objectives of this study were to investigate whether the same mice are identified as frail with both tools and to examine the association of each of the assessment tools with age and frailty-related outcomes. Frailty was measured using both tools in old (~24 months; n = 36) C57BL/6 male mice. After 2 weeks, blood pressure and heart rate were measured and serum samples were collected for analysis of alanine aminotransferase, creatinine, and albumin levels. The mouse frailty phenotype assessment identified no mice as frail but modification of the assessment tool identified six mice as frail. The mouse clinical frailty index identified 16 mice as frail and the agreement between the two scales was 50.0%. Increasing clinical frailty index scores were correlated with low serum alanine aminotransferase, as well as decreased heart rate, and reduced heart rate variance. We conclude that, consistent with equivalent frailty assessment scales in humans, both tools have value but do not necessarily identify the same mice as frail.

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)0.006
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.220
GPT teacher head0.482
Teacher spread0.262 · 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
GenreEmpirical

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

Citations41
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series ASame topicFrailty in Older AdultsFrench-language works237,207