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Record W2974376570 · doi:10.1093/ageing/afz103.217

334 GEMS: Geriatric Emergency Service - Rockwood’s Clinical Frailty Scale and Outcomes

2019· article· en· W2974376570 on OpenAlexaboutno aff
Danielle Reddy, Gráinne Gallagher, Maureen O’Callaghan, Lorna Cornally, Megan Hayes Brennan, Ann Mulholland, Jane Nolan, Cathriona Normoyle, Emer Ahern, Ruth Gibbons

Bibliographic record

VenueAge and Ageing · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriageInstitutionalisationGerontologyVulnerability (computing)Aged careEmergency medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Rockwood’s Clinical Frailty Scale (CFS), which uses clinical descriptors and pictographs, was developed to provide clinicians with an easily applicable tool to stratify older adults according to level of vulnerability. The CFS was validated in a sample of 2305 older participants from the Canadian Study of Health and Aging and was shown to be a strong predictor of institutionalisation and mortality (Rockwood K, 2005). Methods The aim of GEMS is to improve care, outcomes and the patient experience for older people living with Frailty. All people aged 75 years and older who attend as an emergency are screened on triage using the Variable Indicative of Placement Tool (VIP). The GEMS Acute Floor Team respond early to those who screen positive by starting a CGA. At the end of CGA all patients have a score 1 to 9 assigned from the Clinical Frailty Scale (CFS). Results 10,037 patients were triaged in the first two years of the service. 43% screened positive for Frailty. 66% had a CGA. 10% were vulnerable CFS 4, 32% mildly frail CFS 5, 32% moderately frail CFS 6 and 31% severely frail CFS 7. Increasing score on the CFS correlated with increased length of stay, death and institutionalisation. Conclusion The CFS correlates with Length of stay (LOS), mortality and institutionalisation in people aged 75 years and older who attend as an emegency and screen positive for Frailty.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.324
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2019
Admission routes1
Has abstractyes

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