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Record W2744398783 · doi:10.1177/0733464817723089

Discharge Home From Hospital: How DIRE Can It Be?

2017· article· en· W2744398783 on OpenAlexaff
Priti S. Flanagan, Ronald Kelly

Bibliographic record

VenueJournal of Applied Gerontology · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsFraser Health
Fundersnot available
KeywordsBootstrapping (finance)MedicineHospital dischargeSample (material)Residential careGerontologyMedical emergencyEmergency medicineIntensive care medicine

Abstract

fetched live from OpenAlex

AIM: An easy-to-use "DIRE" questionnaire tool was developed to predict an adverse event (AE) within 30 days following discharge from a hospital to the community, among frail elderly individuals aged 65+ years. METHODS: Hospital-administered RAI-HC (Residential Assessment Instrument for Home Care) assessment data from 1,433 individuals were used to develop the tool. RESULTS: The DIRE tool outperformed two other instruments that have been used to predict risk in similar populations. Furthermore, the DIRE index was validated on a hold-out sample and in a bootstrapping analysis. DISCUSSION: In addition to its effectiveness in predicting an AE, the added advantages of the DIRE assessment is that only a small amount of data is required and the data are readily available to clinicians at the point of hospital discharge.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.380
Teacher spread0.321 · 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 designNot applicable
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

Citations1
Published2017
Admission routes1
Has abstractyes

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