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Record W2989467111 · doi:10.1016/j.jcrc.2019.10.016

Changes in frailty among ICU survivors and associated factors: Results of a one-year prospective cohort study using the Dutch Clinical Frailty Scale

2019· article· en· W2989467111 on OpenAlexfundno aff
Wytske W. Geense, Marieke Zegers, Peter Dieperink, Hester Vermeulen, Johannes van der Hoeven, Mark van den Boogaard

Bibliographic record

VenueJournal of Critical Care · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersChicken Farmers of Saskatchewan
KeywordsMedicineProspective cohort studyCohortScale (ratio)Cohort studyGerontologyEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Frailty is an important predictor for the prognosis of intensive care unit (ICU) patients. This study examined changes in frailty in the year after ICU admission, and its associated factors. MATERIALS AND METHODS: Prospective cohort study including adult ICU patients admitted between July 2016-December 2017. Frailty was measured using the Clinical Frailty Scale (CFS), before ICU admission, at hospital discharge, and three and 12 months after ICU admission. Multivariable linear regression was used to explore factors associated with frailty changes. RESULTS: Frailty levels changed among 1300 ICU survivors, with higher levels at hospital discharge and lower levels in the following months. After one year were 42% of the unplanned, and 27% of the planned patients more frail. For both groups were older age, longer hospital length of stay, and discharge location associated with being more frail. Male sex, higher education level and mechanical ventilation were associated with being less frail in the planned patients. CONCLUSION: One year after ICU admission, 42% and 27% of the unplanned and planned ICU patients, respectively, were more frail. Insight in the associated factors will help to identify patients at risk, and may help in informing patients and their family members. REGISTRATION: ClinicalTrials.gov database (NCT03246334).

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)0.002
metaresearch head score (Gemma)0.007
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 score0.035
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.385
Teacher spread0.317 · 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

Citations62
Published2019
Admission routes1
Has abstractyes

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