Background: To describe the demographics and outcomes of assault-related open-globe injuries (OGI) at University Hospital (UH), Newark, New Jersey over a ten-year period.
Methods: The medical records of all subjects presenting to a single university referral center with an OGI were retrospectively analyzed to identify prognostic factors for enucleation and final visual acuity (VA) of no light perception (NLP).
Results: One hundred and forty-eight eyes of 147 patients presented to UH with assault-related OGI. Eighty-two percent of patients were male, and the mean age was 35.9 years. The anatomic site of the wound was zone 3 in the majority (54.1 %) of eyes. Most common type of injury noted was rupture (57.4 %), followed by penetrating injury (35.1 %). Mean initial presenting and final VA in LogMAR were 2.38 ± 0.12 and 2.18 ± 0.16 respectively. Initial Snellen VA was no light perception (NLP) in 57 eyes (38.5 %); four eyes had an initial VA of ≥ 20/40 (2.7 %). Final VA was NLP in 68 eyes (45.9 %) of which 46 were enucleated (31.1 %); 18 eyes (12.2 %) had a final VA of ≥ 20/40. Fifty eyes (33.8 %) underwent pars plana vitrectomy (PPV). Significant risk factors of final VA of NLP or enucleation included initial VA of NLP, perforating or rupture type of OGI, and zone 3 injury. Eyes that sustained penetrating injuries were less likely to have final VA of NLP or require enucleation.
Conclusions: Assault-related OGIs carry an extremely poor visual prognosis and a high rate of enucleations. Only eighteen eyes (12.2 %) recovered VA ≥ 20/40. We found initial VA of NLP and zone 3 injury to be significant predictors of final VA of NLP or undergoing enucleation. Penetrating injuries were less likely to have a final VA of NLP or an enucleation.
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http://dx.doi.org/10.1007/s00417-012-2136-z | DOI Listing |
Drug Saf
January 2025
Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Background: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.
Objective: To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.
Alzheimers Dement
December 2024
Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, VIC, Australia.
Background: Population dementia prevalence is traditionally estimated using cohort studies, surveys, routinely-collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose. We aimed to develop a suite of algorithms using routinely-collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation.
View Article and Find Full Text PDFJMIR Serious Games
December 2024
Department of Psychology, Lund University, Lund, Sweden.
Front Res Metr Anal
December 2024
Teesside University International Business School, TU Online, Teesside University, Middlesbrough, United Kingdom.
Hosp Pediatr
January 2025
Department of Biomedical and Health Informatics, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Objective: Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements from infants aged 90 days and younger with fevers at home or clinics prior to emergency department (ED) visits.
Patients And Methods: We conducted a cross-sectional study using electronic health record (EHR) data from 17 EDs in 10 health systems that are part of the Pediatric Emergency Care Applied Research Network Registry.
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