A Scoping Review of Frailty Assessment Tools for Elderly Orthopedic Inpatients
Bibliographic record
Abstract
Background With the aggravation of population aging in China, the number of elderly perioperative orthopedic patients is increasing, and the growing prevalence of frailty in older patients undergoing orthopedic surgery has attracted increasing attention. Early preoperative assessment and intervention of frailty are of great significance for improving postoperative prognosis and reducing the occurrence of complications in this population. Objective To perform a scoping review of frailty assessment tools for elderly orthopedic inpatients, and to provide a reference for the selection of frailty assessment tools for this group. Methods Seven databases (PubMed, CINAHL, PsycINFO, Scopus, Embase, CNKI and Wanfang Data) were searched for studies on frailty assessment tools for older orthopedic inpatients from 2006 to 2021. Two researchers independently screened the literature and extracted the basic characteristics of the literature (the flint author, publication time, country, basic information, research tools and outcome indicators) and the basic characteristics of involved frailty assessment tools (name, study country, study type, scale dimension, number of items, assessment cut-off value, assessment time, etc.) . Results A total of 1733 studies were retrieved, and 25 of them with 12 frailty assessment tools were included. The analysis showed that there are a variety of assessment tools, and different studies have used different frailty assessment tools. Frailty Phenotype and Frailty Index are the two common tools. The application of accurate and effective tools for frailty screening is crucial to improving preoperative risk stratification and postoperative prognosis. Frailty assessment using the Reported Edmonton Frail Scale, FRAIL Scale, PRISMA-7 Questionnaire or the Groningen Frailty Index can be completed without the use of additional measuring equipment and surveyors with an experience of training. Conclusion The selection of an optimal frailty assessment tool for elderly orthopedic inpatients should be in accordance with patient features, clinical resources and the performance of the tool. However, there is still lack of a gold standard for frailty assessment. Future studies are needed to assess the reliability and validity of existing frailty assessment scales or to develop frailty assessment tools applicable to Chinese older orthopedic inpatients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".