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Frailty as a predictor of future falls in hospitalized patients: A systematic review and meta-analysis
Xiuyan Lan, Hong Li, Zijuan Wang, Ying Chen
Bibliographic record
VenueGeriatric Nursing · 2019
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersEuropean Social FundNational Institute of Diabetes and Digestive and Kidney DiseasesNIHR Oxford Biomedical Research CentreHealth and Social Care Research and Development DivisionNational Health and Medical Research CouncilEconomic and Social Research CouncilMedical Research CouncilGillings School of Public HealthNational Institutes of HealthVetenskapsrådetKuwait UniversityUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiNational Institute of Child Health and Human DevelopmentFundação de Amparo à Pesquisa do Estado de Minas GeraisState Government of VictoriaChina Medical UniversityPublic Health AgencyConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDepartment of Biotechnology, Ministry of Science and Technology, IndiaCouncil for the Development of Social Science Research in AfricaUniversity of BristolAutoritatea Natională pentru Cercetare StiintificăInstituto de Salud Carlos IIINational Natural Science Foundation of ChinaUniversity of CalgaryUniversity of MelbourneHealth Research Council of New ZealandUniversity of New South WalesUniversiti Kebangsaan MalaysiaEuropean Regional Development FundKing's College LondonBrien Holden Vision InstituteThe Wellcome Trust DBT India AllianceNational Research FoundationNational Institute for Health and Care ResearchKasturba Medical College, ManipalEngineering and Physical Sciences Research CouncilNational Institute for Health Research Health Protection Research UnitDeakin UniversityUniversität HeidelbergPublic Health EnglandNational Authority for Scientific Research and InnovationEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBHF Centre of Research Excellence, OxfordNational Institute of Mental HealthDanmarks GrundforskningsfondMinistério da Ciência, Tecnologia e Ensino SuperiorWellcome TrustComunidad de MadridMinisterio de Ciencia, Innovación y UniversidadesXiamen UniversityDepartment of Science and Innovation, South AfricaMinistério da SaúdeChief Scientist Office, Scottish Government Health and Social Care DirectorateBritish Heart FoundationBundesministerium für Bildung und ForschungKing's College Hospital NHS Foundation TrustPublic Health Agency of CanadaFred Hollows FoundationScottish GovernmentMinistero della SaluteQueensland HealthUnited States Agency for International DevelopmentFundação para a Ciência e a TecnologiaBill and Melinda Gates Foundation
KeywordsMeta-analysisMedicineConfidence intervalCINAHLCochrane LibraryOdds ratioHazard ratioMEDLINEScopusInternal medicineFalls in older adultsPoison controlInjury preventionEmergency medicinePsychiatryPsychological intervention
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
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.
metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.057
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 head0.046
GPT teacher head0.351
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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
Citations52
Published2019
Admission routes1
Has abstractyes