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Record W2116870538 · doi:10.1093/ageing/afi020

The Sherbrooke Questionnaire predicts use of emergency services

2005· article· en· W2116870538 on OpenAlexaboutno aff
Lesley Walker, Konrad Jamrozik, David Wingfield

Bibliographic record

VenueAge and Ageing · 2005
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
FundersWest London Research Network
KeywordsMedicineAttendanceLogistic regressionBoroughEmergency departmentOdds ratioPopulationOddsEmergency medicineMedical emergencyEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: the National Service Framework for Older People mandates the introduction of 'intermediate care services' to reduce emergency admissions to hospital from the population aged 75 years or more. We evaluated the predictive performance of one of the screening instruments used to identify older people who might most benefit from such services. METHODS: using multiple logistic regression, we compared responses to the six-item, self-administered Sherbrooke Questionnaire with subsequent patterns of emergency attendance and admission to hospital among the elderly population of one borough in West London. RESULTS: excess risk of both emergency attendance and admission became significant when two or more risk factors were present, and rose progressively with each additional factor, regardless of their nature. With each additional year of age, risks of emergency attendance and admission to hospital increased by 8% (95% CI = 6-10) and 9% (95% CI = 7-12), respectively. There were also significant independent risks associated with reporting memory problems (typical odds ratio (OR) 1.41, 95% CI = 1.14-1.75) and taking three or more medications (OR 1.65, 95% CI = 1.34-2.02), as well as large risks associated with attendance or admission in the year before screening. CONCLUSION: the Sherbrooke Questionnaire is a good measure of likely need for emergency visits to hospital among the elderly. However, programmes attempting to reduce such events should also take into account the individual's recent history of emergency attendance at hospital.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.174

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 studies0.0000.000
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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 designOther design
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

Citations26
Published2005
Admission routes1
Has abstractyes

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