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Research advances of perioperative frailty assessment tools

2019· article· en· W3032115213 on OpenAlexaboutno aff
Rui Zhu, Xixue Zhang

Bibliographic record

VenueGuoji mazuixue yu fusu zazhi · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsPerioperativeMedicineTimed Up and Go testGerontologyScale (ratio)PopulationPhysical therapyPhysical medicine and rehabilitationIntensive care medicineSurgeryEnvironmental healthBalance (ability)

Abstract

fetched live from OpenAlex

Frailty refers to the state of decreased physiological function reserve caused by the increase of age-related multisystemic accumulated defects. The number of elderly patients with frailty undergoing elective operations is gradually increasing. The stages of frailty have strong correlation with the prognosis and mortality after surgery. By summarizing the frailty assessment scales used in foreign countries, we provide reference to the selection of clinical frailty assessment tools in Chinese population. This article reviewed two frailty models (phenotype model and the cumulative deficit model) and three assessment tools [Simple Frailty Questionnaire (FRAIL), Edmonton Frail Scale (EFS) and Program on Research for Integrating Services for the Maintenance of Autonomy (PRISMA)- 7 Scale]. In addition, three assistant indexes [handgrip strength, gait speed and Timed Up-And-Go Test (TUGT)] are introduced. The present review provides reference for the studies on perioperative complications in patients with frailty. Key words: Aged; Frailty; Risk assessment

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)0.012
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.085
GPT teacher head0.434
Teacher spread0.349 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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