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Record W2726090185 · doi:10.1093/geroni/igx004.3303

SCREENING AND ASSESSMENT IN THE EMERGENCY DEPARTMENT

2017· article· en· W2726090185 on OpenAlexaffabout
Andrew P. Costa

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEmergency departmentMedicineContext (archaeology)Multinational corporationCzechGeriatric careFamily medicineEmergency medicineMedical emergencyNursingGeography

Abstract

fetched live from OpenAlex

Guidelines suggest that older patients presenting to the emergency department (ED) should be screened to identify geriatric complexity and prioritize specialized geriatric resources. Screening and assessment tools have been developed, but few have been compared from a multinational context. interRAI has developed an integrated assessment system for geriatric care in the ED. It includes two companion tools – the ED Screener and the ED Contact Assessment. We conducted two prospective studies with over 4,000 older ED patients from Australia, Belgium, Canada, Czech Republic, Germany, Iceland, India, Italy, Spain, and Sweden. Patients were assessed at ED admission with a standardized screening or assessment. Outcomes were examined for admitted patients and those discharged home. The probability of negative patient outcomes was detectable at the multinational level with moderate accuracy. Agreement with independent ratings by geriatricians was high. Results demonstrate the utility of incorporating standardized geriatric instruments in routine clinical examination across contexts.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.063
GPT teacher head0.386
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 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

Citations0
Published2017
Admission routes2
Has abstractyes

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