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Profiling Acute Presenting Symptoms of Geriatric Patients Attending an Urban Hospital Emergency Department

2009· article· en· W2321778785 on OpenAlexfundno aff
Chik Loon Foo, Kim Chai Chan, Hsin Kai Goh, Eillyne Seow

Bibliographic record

VenueAnnals of the Academy of Medicine Singapore · 2009
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
FundersMcMaster University
KeywordsMedicineTriageEmergency departmentPopulationEmergency medicinePediatricsRetrospective cohort studyHealth careInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the profile of geriatric patients warded to the emergency department (ED) of an Asian acute care general hospital and determine if they are 'more ill', more likely to have atypical presentations and have a higher utilisation of healthcare resources when compared to a younger group of patients. MATERIALS AND METHODS: This is a retrospective chart review of consecutive patients aged 45 years and above presenting to the study ED over a period of 4 weeks from 4 June 2006 to 1 July 2006. The following data were obtained: (i) demographics, (ii) mode of arrival and triage acuity, (iii) presence of co-morbidities, (iv) investigations ordered in the ED, (v) clinical symptoms and diagnoses, (vi) disposition, (vii) length of hospital stay, (viii) injuries and outcomes of elderly fallers. The study population was divided into 2 groups--a study group with patients aged 65 years and above, and a control group with patients aged 45 to 64. RESULTS: There were 2847 patients in the study group and these were compared against 2875 in the control group. Those 65 years and above had greater representation in the ED population compared to the general population. In the study group, the proportion of females, the number arriving by ambulance and the likelihood of having a higher triage acuity increased with age. The elderly had higher rates of co-morbidities. They also had a higher resource utilisation rate. Falls was their commonest presenting complaint. CONCLUSION: It is crucial that EDs recognise the special needs of elderly patients due to the growing ageing population. Healthcare policy makers when allocating resources should take into account the profile of elderly patients presenting to an ED and their resource utilisation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.090
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.036
GPT teacher head0.351
Teacher spread0.315 · 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

Citations33
Published2009
Admission routes1
Has abstractyes

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