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Record W2902499890 · doi:10.7759/cureus.3642

Characterizing the Long-term Care and Community-dwelling Elderly Patients' Use of the Emergency Department

2018· article· en· W2902499890 on OpenAlexaffabout
Sachin Trivedi, C Michael Roberts, Erwin Karreman, Kish Lyster

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of SaskatchewanRegina Qu'Appelle Health Region
Fundersnot available
KeywordsMedicineEmergency departmentTerm (time)Medical emergencyLong-term careGerontologyEmergency medicineNursing

Abstract

fetched live from OpenAlex

Introduction Elderly patients, particularly those in long-term care (LTC), are a growing proportion of patients who present to the emergency department (ED). This population is medically complex, with high burdens on ED resources and patient flow. This study sought to characterize how elderly LTC and community-dwelling (CD) patients use ED services. Materials and methods This was a retrospective cohort study that assessed approximately 200 senior (age>65) ED visits. These patients were either residing in LTC facilities or they were CD. All participants lived in the same, medium-sized Canadian city. Data indicating demographic information, acuity of presentation, and administrative parameters (such as disposition status or length of stay) were collected and analyzed. Results A few statistically significant differences between the populations were noted. This included mean age, which was 82.6 years in the LTC population and 77.3 for the CD group (p<0.001). There were 27 repeat visits among patients in the LTC group, compared to six from the CD patients (p<0.001). In the LTC population, 75 patients required transport from emergency medical services (EMS) compared to 41 from the control group (p<0.001). Conclusion LTC patients re-present to the ED and use EMS services more frequently than their CD counterparts. This difference indicates potential areas to target for future quality improvement work to help enhance care to this vulnerable population.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.362
Teacher spread0.301 · 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.

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

Citations7
Published2018
Admission routes2
Has abstractyes

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