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Self management and patient understanding of diabetes in the older person

2010· article· en· W2160077865 on OpenAlexaboutno aff
Jonathan Hewitt, Liam Smeeth, Nish Chaturvedi, C J Bulpitt, Astrid Fletcher

Bibliographic record

VenueDiabetic Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersNational Institute for Health and Care ResearchWellcome Trust
KeywordsMedicineDiabetes mellitusGerontologyDiabetes managementCognitionQuarter (Canadian coin)DiseaseType 2 diabetesCognitive impairmentFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

AIM: To examine knowledge and management of diabetes by older people. METHODS: A representative sample of 1047 people with Type 2 diabetes, aged 75 years and over, were asked a series of questions relating to their diabetes management and their understanding of self management. The impact of cognitive impairment and socio-economic status were assessed. RESULTS: The majority of people, 1015 (96.9%), were under the care of a health professional and 1018 (97.2%) were taking insulin, tablets, controlling their diet or a combination. Cognitive impairment (Mini-Mental State Examination ≤ 23) was found in 235 (22.5%) people. Recent eye, foot and dietician assessment was reported by 813 (77.7%), 836 (79.7%) and 326 (31.1%) people, respectively. A quarter overall and 70% of those taking insulin tested their blood glucose. In the insulin group, 78 (54.2%) reported hypoglycaemia and those with cognitive impairment gave more incorrect responses when asked about diabetes management. Socio-economic status made very little difference to any of these outcomes. CONCLUSIONS: Most older people with diabetes, regardless of their socio-economic status, are under the care of a healthcare professional and use medication or diet to manage their disease. Large numbers also attend foot and eye examinations. However, over one fifth of older people with diabetes have cognitive impairment. Older people had a reasonable understanding of their diabetes management but this was worse in those people with cognitive impairment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.281

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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

Citations82
Published2010
Admission routes1
Has abstractyes

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