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Record W2085057514 · doi:10.1371/journal.pone.0096291

Understanding the Evolution of Multimorbidity: Evidences from the North West Adelaide Health Longitudinal Study (NWAHS)

2014· article· en· W2085057514 on OpenAlexaff
Guillaume Ruel, Jean‐Frédéric Lévesque, Nigel Stocks, Caroline Sirois, Edeltraut Kröger, Robert J. Adams, Anne Taylor

Bibliographic record

VenuePLoS ONE · 2014
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversité du Québec à RimouskiCentre Hospitalier de l’Université de MontréalMinistry of Health and Social ServicesUniversité LavalInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsMultimorbidityMedicineAsthmaDiabetes mellitusCOPDLongitudinal studyComorbidityDiseaseInternal medicinePulmonary diseaseMoodChronic diseaseMood disordersPediatricsDemographyEndocrinologyPsychiatryPathologyAnxiety

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study is to describe the evolution of multimorbidity. STUDY DESIGN AND SETTING: Data from 1854 South Australians who participated in the North West Adelaide longitudinal Health Study (NWAHS) was collected between baseline (2000-2002) and follow-up (2008-2010). Status for eight chronic diseases (CDs) was determined by biomedical measurement or self-report. Chronic disease (CD) mean age of occurrence and order of appearance was investigated. RESULTS: The prevalence of multimorbidity increased from 32% to 64% during the 7.8±1.1 years of follow-up. The estimated mean age of onset of a new CD was significantly older for hypertension, cardiovascular disease (CVD) and chronic obstructive pulmonary disease (COPD) and younger for hypercholesterolemia, asthma and other mental problem. Hypercholesterolemia was more likely to develop as a first than as a subsequent CD (39%vs.16%, p<0.0001) while CVD (1%vs.5%, p<0.0001), diabetes (5%vs.11%, p<0.001) and COPD (6%vs.16%, p<0.0001) were less likely. The presence of mood disorders at baseline was associated with an increased risk of developing other mental disorders (36%vs.12%, p<0.0001), diabetes (18%vs.9%, p<0.01) and asthma (30%vs.21%, p<0.05). CONCLUSION: Longitudinal data could be used to study the evolution of multimorbidity and could provide information on CDs mean age of occurrence, order of appearance and impact on the development of future CDs.

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)0.000
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 score0.014
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.357
GPT teacher head0.347
Teacher spread0.010 · 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

Citations32
Published2014
Admission routes1
Has abstractyes

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