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Record W1973178720 · doi:10.1177/0898264314558202

Frailty in an Older Inpatient Population

2014· article· en· W1973178720 on OpenAlexaboutno aff
David Basic, Chris Shanley

Bibliographic record

VenueJournal of Aging and Health · 2014
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyPopulationMedicinePsychologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the impact of frailty, measured using the Canadian Study of Health and Aging Clinical Frailty Scale, on outcomes of older people hospitalized with acute illness. METHOD: Consecutive patients were randomly allocated to a model development sample or a model validation sample. Multivariate analyses were used to model in-hospital mortality, new nursing home placement, and length of stay. Variables selected in the development samples were tested in the validation samples. RESULTS: The mean age of all 2,125 patients was 82.9 years. Most (93.6%) were admitted through the emergency department. Frailty predicted in-hospital mortality (odds ratio [OR] = 2.97 [2.11, 4.17]), new nursing home placement (OR = 1.60 [1.14, 2.24]), and length of hospital stay (hazard ratio = 0.87 [0.81, 0.93]). DISCUSSION: Frailty is a strong predictor of adverse outcomes in older people hospitalized with acute illness. An increased awareness of its impact may alert clinicians to screen 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 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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.373
Teacher spread0.324 · 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 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

Citations214
Published2014
Admission routes1
Has abstractyes

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