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Record W4289260565 · doi:10.1186/s12877-022-03321-y

Incidence and clinical characteristics of fall-related injuries among older inpatients at a tertiary grade a hospital in Shandong province from 2018 to 2020

2022· article· en· W4289260565 on OpenAlexfundno aff
Hong Lyu, Yan Dong, Wenhong Zhou, Chuanxia Wang, Hong Jiang, Ping Wang, Yanhong Sun

Bibliographic record

VenueBMC Geriatrics · 2022
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
FundersMedical and Health Science and Technology Development Project of Shandong ProvinceUniversity of Toronto
KeywordsMedicineIncidence (geometry)Fall preventionAdverse effectEmergency medicineInjury preventionRehabilitationOccupational safety and healthPoison controlFalling (accident)Physical therapyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Falls are an important cause of injury and death of older people. Hence, analyzing the multifactorial risk of falls from past cases to develop multifactorial intervention programs is clinically significant. However, due to the small sample size, there are few studies on fall risk analysis of clinical characteristics of fallers, especially among older hospitalized patients. METHODS: We collected data on 153 inpatients who fell (age ≥ 60 years) from the hospital nursing adverse event reporting system during hospitalization at Shandong Provincial Hospital Affiliated to Shandong First Medical University, China, from January 2018 to December 2020. Patient characteristics at the time of the fall, surrounding environment, primary nurse, and adverse fall events were assessed. The enumeration data were expressed as frequency and percentage, and the chi-squared was performed between recurrent fallers and single fallers, and non-injurious and injurious fall groups. RESULTS: Cross-sectional data showed 18.3% of the 153 participants experienced an injurious fall. Compared with single fallers, a large proportion of older recurrent fallers more often experienced preexisting conditions such as cerebrovascular disease or taking hypoglycemic drugs. They were exposed to higher risks and could experience at least 3 fall times in 3 months. Besides, the credentials of their responsible nurses were often higher. Factors that increased the risk of a fall-related injury were hypoglycemic drugs (OR 2.751; 95% CI 1.114-6.795), and nursing adverse events (OR 47.571; 95% CI 14.392-157.247). Older inpatients with bed rails (OR 0.437; 95% CI 0.190-1.005) or falling at the edge of the bed (OR 0.365; 95% CI 0.138-0.964) were less likely to be injured than those without bed rails or not falling at the edge of the bed. Fall risks were significantly correlated with more severe fall-related injuries. Older patients with moderate (OR 5.517; CI 0.687-44.306) or high risk (OR 2.196; CI 0.251-19.219) were more likely to experience fall-related injuries than those with low risk. CONCLUSIONS: Older inpatient falls are an ongoing challenge in hospitals in China. Our study found that the incidence of fall-related injuries among inpatients aged ≥ 60 years remained at a minor level. However, complex patient characteristics and circumstances can contribute to fall-related injuries. This study provides new evidence on fall-related injuries of older inpatients in China. Based on the factors found in this study, regular fall-related injury epidemiological surveys that investigate the reasons associated with the injuries were crucial when considering intervention measures that could refine fall-related injuries. More prospective studies should be conducted with improved and updated multidisciplinary fall risk assessment and comprehensive geriatric assessment as part of a fall-related injury prevention protocol.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.320
Teacher spread0.305 · 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

Citations22
Published2022
Admission routes1
Has abstractyes

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