ArticleCME journal. Geriatric medicine2008
Coding Geriatric syndromes: How good are we?
Article in CME journal. Geriatric medicine, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges.BMC medical informatics and decision making · 2026Pooled it
- The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.Age and ageing · 2024Pooled it
- Extracting geriatric syndromes from electronic health records: a scoping review.European geriatric medicine · 2026Article
- Geriatric syndromes extraction from discharge summaries: a new dataset, annotation scheme and initial findings.Frontiers in digital health · 2026Article
- Identifying health conditions in older adults in textual health records using deep learning-based natural language processing.Computational and structural biotechnology journal · 2025Article
- Rising to the challenge of defining and operationalising multimorbidity in a UK hospital setting: the ADMISSION research collaborative.European geriatric medicine · 2024Article
- Observational
- Researching multimorbidity in hospital: can we deliver on the promise of health informatics?European geriatric medicine · 2023Article
- Social position and geriatric syndromes among Swedish older people: a population-based study.BMC geriatrics · 2019Article
- Is grip strength a good marker of physical performance among community-dwelling older people?The journal of nutrition, health & aging · 2012Article
- Features and outcomes of unplanned hospital admissions of older people due to ill-defined (R-coded) conditions: retrospective analysis of hospital admissions data in England.BMC geriatrics · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
High quality coding of hospital activity is important because the data is used for resource allocation and measuring performance. There is little information on the quality of coding of admissions of frail older people who have multiple diagnoses, co-morbidities and functional impairment. Presence or absence of four geriatric syndromes and eight medical conditions was noted on case note review (CNR). Discharge summaries (DS) and hospital coding (HC) were reviewed and compared with the CNR. Forty patients had at least one geriatric syndrome noted in the DS; 16 (40.0%) were captured by the HC. Of 57 patients with at least one medical condition noted in the DS, 52 (91.2%) were captured by the HC (p<0.0001 for difference in HC capture rates). We have demonstrated poor capture of information on geriatric syndromes compared to medical conditions in discharge summaries and hospital coding and propose a problem list bookmark approach to improve this.
Identifiers
22003315PMC3191527What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.