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Record W2804815440 · doi:10.1186/s13643-018-0712-y

How do we define and measure sarcopenia? Protocol for a systematic review

2018· review· en· W2804815440 on OpenAlexaff
Paulo Roberto Carvalho do Nascimento, Stéphane Poitras, Martin Bilodeau

Bibliographic record

VenueSystematic Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsBruyèreUniversity of Ottawa
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSarcopeniaMedicineCINAHLObservational studyMEDLINESystematic reviewProtocol (science)GeriatricsGerontologyPopulationPathologyAlternative medicineNursingPsychological interventionEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The loss of muscle mass is a natural aging consequence. A reduction of muscle mass that surpasses the physiological rate is considered the key factor responsible for the development of a geriatric syndrome called sarcopenia. However, a new understanding of the importance of muscle quality over quantity is rising; as a result, different definitions for sarcopenia has been used. Due to the negative impact on elder's health and quality of life, the number of research investigating the causes, prevalence, and management of sarcopenia is increasing, although a consensus on sarcopenia definition is still missing. This systematic review will assess observational studies reporting the presence of sarcopenia aiming to verify how sarcopenia is defined, the diagnosis criteria, and the tools used for assessment. In addition, we will investigate the influence of the definition and diagnostic tools on the prevalence rate. METHODS: Keywords related to the condition, population, and type of study will be combined to build a search strategy for each of the following databases MEDLINE, EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and Google Scholar. Two independent reviewers will analyze the retrieved papers for eligibility and the methodological quality of eligible studies. The definition of sarcopenia and diagnostic tools used in each study and the prevalence estimates will be extracted. Descriptive statistics will be used to report the definitions of sarcopenia, diagnostic tools, and whether these influence or not, the prevalence rates. DISCUSSION: Sarcopenia is receiving greater attention in geriatrics research in recent years. Therefore, it is important to investigate how this condition is defined in the literature and whether these definitions can interfere with the reported estimates devoting more efforts on the topic. The results of this study can help to determine the most used definitions of sarcopenia reported in the literature, its strengths and limitations, and open a discussion about a need for a more valid, easy, and suitable one. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42015020832.

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)0.014
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0240.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.303
GPT teacher head0.487
Teacher spread0.184 · 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

Citations29
Published2018
Admission routes1
Has abstractyes

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