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Record W2118073115 · doi:10.1097/hjr.0b013e3283304060

Trends in referral to outpatient cardiac rehabilitation in the Hunter Region of Australia, 2002-2007

2010· article· en· W2118073115 on OpenAlexfundno aff
Natalie Johnson, Kerry Inder, Steven J. Bowe

Bibliographic record

VenueEuropean Journal of Cardiovascular Prevention & Rehabilitation · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
FundersHunter New England Local Health DistrictHeart and Stroke Foundation of Canada
KeywordsReferralMedicineStroke (engine)Myocardial infarctionRehabilitationAtrial fibrillationLogistic regressionHeart failureEmergency medicinePhysical therapyInternal medicineFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Cardiac rehabilitation (CR) is an underutilized evidence-based treatment. We described trends in referral to outpatient CR (OCR) and the factors associated with referral. DESIGN: Cross-sectional survey data provided by Hunter residents aged 20 years or older discharged from public hospitals in the region between 2002 and 2007 with an OCR eligible diagnosis were extracted from the Hunter New England Heart and Stroke Register database. METHODS: Trends in referral were determined using the chi test for trend. Factors associated with referral were examined using multiple logistic regression. RESULTS: Sixty-five percent (4971 of 7678) of patients provided sufficient data for inclusion in the analysis. Approximately half of the patients reported being referred to OCR. No increase over time was observed. Factors associated with referral were age less than 70 years, male sex, being married, urban residence, at least one admission to the tertiary referral hospital for cardiology, at least one admission for acute myocardial infarction, revascularization, no admissions for congestive heart failure, a self-reported history of high cholesterol, and no history of stroke or atrial fibrillation. CONCLUSION: Access to this treatment of proven benefit remained suboptimal despite the provision of new programs and expansion of existing programs. Automatic referral, which is recommended in Australia, should be standard practice.

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)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.333
Teacher spread0.302 · 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

Citations18
Published2010
Admission routes1
Has abstractyes

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