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Low muscle mass in older adults and mortality: a systematic review and meta-analysis

2021· review· en· W3212782631 on OpenAlexaboutno aff
Nicolas Yamada Tanigava, Felipe Santana, Melissa Orlandin Premaor, Rosa Maria Rodrigues Pereira

Bibliographic record

VenueRevista de Medicina · 2021
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSarcopeniaMedicineMeta-analysisBody mass indexMuscle massGrip strengthCohortMass indexInternal medicineCohort studyHand strengthGerontologyDemographyPhysical therapy

Abstract

fetched live from OpenAlex

Sarcopenia comprises a loss of muscle function and muscle mass. So far, the association between loss of muscle mass and mortality in older adults is inconsistent. A meta-analysis was performed to assess whether muscle mass measured by appendicular skeletal muscle mass index (ASMI) is associated with higher mortality in older adults. Articles of interest were searched for in two databases (PudMed® and Embase®). Cohort and case-control studies reporting ASMI and mortality and enrolling community-dwelling adults aged 65 years or more were included. Nine articles were eligible and included for analysis (n=10,028). All but one study were considered of high quality by Newcastle-Ottawa Scale assessment. We calculated the standardized mean difference (SMD) for ASMI between dead and living individuals during follow-up across studies. A reduced pooled ASMI in individuals who died as compared to those who survived was found (ASMI SMD=-0.18, CI95% -0.23 to -0.12, REM). A meta-regression was performed including ASMI SMD, grip strength SMD, body mass index (BMI), sex, study quality, method used to assess ASMI, site of study and age. BMI and ethnicity were found to significantly impact the difference in ASMI between dead and living individuals. These results reinforce the prognostic importance of assessing muscle mass in older adults.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0150.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.105
GPT teacher head0.417
Teacher spread0.312 · 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 teacher head, not a consensus.

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
Published2021
Admission routes1
Has abstractyes

Explore more

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