Background: Pre-hospital laryngoscopic endotracheal intubation (ETI) is potentially a life-saving procedure but is a technique difficult to acquire. This study aimed to obtain a recommendation for the number of times ETI should be practiced by constructing the learning curve for endotracheal intubation by paramedics, as well as to report the change in the frequency of complications possibly associated with intubation over the training period.
Methods: Under training conditions, 32 paramedics performed a total of 1,045 ETIs in an operating room. Trainees performed ETIs until they succeeded in 30 cases. For each patient, the number of laryngoscopic maneuvers and any complications potentially associated with ETI were recorded. We built a generalized logistic model to construct the learning curve for ETI and the frequency of complications.
Results: During the training on the first 30 patients the rate of ETI success at the first attempt improved from 71% to 87%, but there was little improvement during the first 13 cases. The frequency of complications decreased from 53% to 31%. More laryngoscopic maneuvers and longer operation time increased complications.
Conclusions: It seems that 30 live experiences of performing an ETI is sufficient for obtaining a 90% ETI success rate, but there seems to be little benefit with fewer than 13 experiences. The frequency of complications remained at a high level even after the training. It is desirable to conduct a more detailed and rigorous evaluation of the benefit of pre-hospital ETI by controlling for the skill level of paramedics.
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http://dx.doi.org/10.1186/1865-1380-6-38 | DOI Listing |
Sci Rep
January 2025
Department of Orthopaedics, Traditional Chinese Medical Hospital of Gansu Province, Qilihe District, Guazhou Street 418, Lanzhou, 730050,, Gansu, China.
Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Korea.
Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied.
View Article and Find Full Text PDFComput Biol Med
January 2025
Thai Nguyen University of Information and Communication Technology, Thai Nguyen City, Viet Nam. Electronic address:
Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Operations Research Group, Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark.
Background: Around 7% of the global population has congenital hemoglobin disorders, with over 300,000 new cases of α-thalassemia annually. Diagnosis is costly and inaccurate in low-income regions, often relying on complete blood count (CBC) tests. This study employs machine learning (ML) to classify α-thalassemia traits based on gender and CBC, exploring the effects of grouping silent- and non-carriers.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Urology, Health Science University Eskisehir City Health Application and Research Center, 26080 Eskisehir, Turkey.
To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected.
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