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Record W2149377948 · doi:10.1345/aph.19140

Recent Advances in Geriatrics: Drug-Related Problems in the Elderly

2000· review· en· W2149377948 on OpenAlexaboutno aff
Joseph T. Hanlon, Leslie A. Shimp, Todd P. Semla

Bibliographic record

VenueAnnals of Pharmacotherapy · 2000
Typereview
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsMedicinePolypharmacyDrugPharmacyGeriatricsAmbulatoryDelphi methodAdverse drug eventAdverse effectIntensive care medicineFamily medicinePharmacologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To review recent articles examining drug-related problems in the elderly and comment on their potential impact on geriatric pharmacy practice. DATA SOURCES: Six articles published in 1997 and 1998. DATA SYNTHESIS: One study estimated that the cost of drug-related morbidity and mortality with the services of consultant pharmacists was $4 billion, compared with $7.6 billion without the services of consultant pharmacists. A study of ambulatory elderly patients with polypharmacy documented that 35% reported experiencing at least one adverse drug event within the previous year. Another study of ambulatory elderly found that in those with discontinued medications, adverse drug withdrawal events were uncommon. Two studies, one from Canada and one from the US, describe the development, by consensus, of explicit criteria for defining and identifying inappropriate drug use in the elderly (i.e., drugs to avoid, drugs with dose limits, drug-drug and drug-disease interactions). Finally, a modified Delphi survey of an expert panel reached consensus on 18 potential risk factors for drug-related factors in long-term care facility residents. CONCLUSIONS: Drug-related problems are considerable for elderly patients. Data from published studies should provide some guidance for today's practitioners as well as direction regarding future research.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.248
GPT teacher head0.513
Teacher spread0.266 · 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.

Study designNot applicable
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

Citations65
Published2000
Admission routes1
Has abstractyes

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