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Record W2163698050 · doi:10.1093/gerona/60.9.1145

Diabetes Is Common in Elderly Persons

2005· letter· en· W2163698050 on OpenAlexaboutno aff
Graydon S. Meneilly

Bibliographic record

VenueThe Journals of Gerontology Series A · 2005
Typeletter
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal functionDiabetes mellitusAdverse effectIntensive care medicineDosingPopulationInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

DIABETES is common in elderly persons. By the age of 75, approximately 25% of the U.S. population will be afflicted with this illness (1). Because of its associated complications, diabetes in the aged has a significant impact on quality of life (2). Therefore, further investigation is timely. Three articles in this issue of the Journal address important aspects of this burgeoning health issue. In the first article, Corsonello and colleagues (3) highlight the prevalence of concealed renal failure in elderly patients with diabetes and emphasize the associated increased risk for adverse drug reactions. Adverse drug reactions are the commonest iatrogenic complication in elderly medical inpatients (4,5). The recently published Canadian Adverse Events Study (5) demonstrated that the most common adverse drug reactions in elderly persons are renal toxicity and inappropriate dosing of medications based on a lack of understanding of impairments in underlying renal function. It has been known for many years that renal function declines with age and that serum creatinine is a relatively poor marker of renal function because of decreases in muscle mass (6). Corsonello and colleagues used a nomogram to calculate glomerular filtration rate (GFR), and demonstrated that concealed renal dysfunction is present in nearly 20% of older patients with diabetes. These patients had a substantially increased risk of adverse drug reactions. This study suggests that, if we are to substantially reduce the risk of iatrogenic events in hospital, we should focus on elderly patients with diabetes. Our efforts should be particularly directed to patients who are taking larger numbers of medications because these are the patients most at risk.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.166
GPT teacher head0.413
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations2
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series ASame topicPharmaceutical Practices and Patient OutcomesFrench-language works237,207