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Record W4322720047 · doi:10.1007/s43678-023-00458-6

Agreement and prognostic accuracy of three ED vulnerability screeners: findings from a prospective multi-site cohort study

2023· article· en· W4322720047 on OpenAlexafffundabout
Fabrice Mowbray, George Heckman, John P. Hirdes, Andrew P. Costa, Olivier Beauchet, Patrick Archambault, Debra Eagles, Han Ting Wang, Jeffrey J. Perry, Samir K. Sinha, Micaela Jantzi, Paul C. Hébert

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsBruyèreUniversity of TorontoUniversity Health NetworkCentre intégré de santé et de services sociaux de Chaudière-AppalachesOttawa HospitalUniversité LavalUniversité de MontréalMcGill UniversityUniversity of OttawaJewish General HospitalUniversity of WaterlooMcMaster UniversityResearch Institute for AgingImpact
FundersCanadian Frailty Network
KeywordsMedicineEmergency departmentLogistic regressionProspective cohort studyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Objectives To evaluate the agreement between three emergency department (ED) vulnerability screeners, including the InterRAI ED Screener, ER2, and PRISMA-7. Our secondary objective was to evaluate the discriminative accuracy of screeners in predicting discharge home and extended ED lengths-of-stay (> 24 h). Methods We conducted a nested sub-group study using data from a prospective multi-site cohort study evaluating frailty in older ED patients presenting to four Quebec hospitals. Research nurses assessed patients consecutively with the three screeners. We employed Cohen's Kappa to determine agreement, with high-risk cut-offs of three and four for the PRISMA-7, six for the ER2, and five for the interRAI ED Screener. We used logistic regression to evaluate the discriminative accuracy of instruments, testing them in their dichotomous, full, and adjusted forms (adjusting for age, sex, and hospital academic status). Results We evaluated 1855 older ED patients across the four hospital sites. The mean age of our sample was 84 years. Agreement between the interRAI ED Screener and the ER2 was fair (K = 0.37; 95% CI 0.33–0.40); agreement between the PRISMA-7 and ER2 was also fair (K = 0.39; 95% CI = 0.36–0.43). Agreement between interRAI ED Screener and PRISMA-7 was poor (K = 0.19; 95% CI 0.16–0.22). Using a cut-off of four for PRISMA-7 improved agreement with the ER2 (K = 0.55; 95% CI 0.51–0.59) and the ED Screener (K = 0.32; 95% CI 0.2–0.36). When predicting discharge home, the concordance statistics among models were similar in their dichotomous (c = 0.57–0.61), full (c = 0.61–0.64), and adjusted forms (c = 0.63–0.65), and poor for all models when predicting extended length-of-stay. Conclusion ED vulnerability scores from the three instruments had a fair agreement and were associated with important patient outcomes. The interRAI ED Screener best identifies older ED patients at greatest risk, while the PRISMA-7 and ER2 are more sensitive instruments.

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)0.013
metaresearch head score (Gemma)0.029
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 score0.027
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.085
GPT teacher head0.362
Teacher spread0.277 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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