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Major Health Risks in Aging Persons With Intellectual Disabilities: An Overview of Recent Studies

2010· article· en· W2095581629 on OpenAlexaff
Meindert Haveman, Tamar Heller, Lynette Lee, Marian A. Maaskant, Shahin Shooshtari, André Strydom

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2010
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOverweightObesityDiseaseMedicineGerontologyEnvironmental healthPopulationPathology

Abstract

fetched live from OpenAlex

Abstract The authors examined the health‐related literature on aging and intellectual disabilities (ID) published since 1999, with specific focus on examining findings on age‐associated health risk factors, such as cardiovascular, gastrointestinal, and musculoskeletal system health issues, and age‐related oral health. They also examined studies of lifestyle health risks, primarily the contributions to overweight or obesity. Although the review revealed varying differences in the prevalence of health risk factors, significant evidence is emerging that cardiovascular disease is as prevalent among people with ID and is as common a cause of death as in the general population. However, the review showed that the variations in prevalence were culturally dependent. Digestive system problems were evident with high occurrence rates of helicobacter pylori, gastroesophageal reflux disease, and constipation. The review revealed a growing body of work on health risk factors, such as overweight and obesity, which are often linked to the onset of a variety of diseases and impairing conditions. Healthier lifestyles, better nutrition and more exercise, and greater surveillance of health risks were seen as ways to improve the health status of aging adults with ID.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.008
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.324
GPT teacher head0.521
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations221
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Policy and Practice in Intellectual DisabilitiesSame topicDown syndrome and intellectual disability researchFrench-language works237,207