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Medical imaging (X-ray, CT, MRI), electronic health records, clinical trials, ECG/EEG, pathology
12,317 datasets
A narrative literature review synthesizing evidence on cardiovascular complications of Long COVID. The document, authored by Jie Sheng and published on figshare in June 2026, covers symptom profiles, underlying mechanisms, and clinical management strategies. It is based on a targeted search of PubMed/MEDLINE, Web of Science, Scopus, Embase, and Google Scholar for articles published up to January 2026.
A study by Sen Yang Xiao, uploaded on 2026-05-29, compares ChatGPT, DeepSeek, and Google Search in answering cartilage repair questions. The dataset contains results from a matched three-way comparison across cartilage tissue engineering (2023) and cartilage repair surgery (2024) domains. It includes blinded quality scores for accuracy, safety, and hallucination, as well as readability analysis.
A study comparing ChatGPT, DeepSeek, and Google Search on answering cartilage repair questions. The dataset includes results from a matched three-way comparison across two medical domains, with quality scored by three blinded raters. The 23.6 KB document was authored by Sen Yang Xiao and last updated in May 2026.
A 2026 study by Sen Yang Xiao compares ChatGPT, DeepSeek, and Google Search on cartilage repair questions. The analysis covers two domains—cartilage tissue engineering (2023) and cartilage repair surgery (2024)—using a dual-axis framework for classification, blinded quality scoring, and readability assessment. The dataset is a 23.3 KB document summarizing the methodology and results of this three-way platform evaluation.
A 2023-2024 study by Sen Yang Xiao compares ChatGPT (GPT-4 API), DeepSeek (V3 API), and Google Search on answering cartilage repair questions. The dataset contains results from a matched three-way comparison across cartilage tissue engineering and cartilage repair surgery domains, including quality scoring and readability analysis. It was last updated on May 29, 2026.
A matched three-way comparison of ChatGPT (GPT-4 API), DeepSeek (V3 API), and Google Search on 20 frequently asked questions in cartilage tissue engineering (2023) and cartilage repair surgery (2024). The dataset contains blinded quality scores using the Accuracy-Safety-Hallucination framework and readability analysis via the Flesch-Kincaid formula. It was authored by Sen Yang Xiao and uploaded to figshare in May 2026.
A 2023-2024 comparative study evaluating ChatGPT, DeepSeek, and Google Search responses to cartilage repair questions. The dataset contains the results of a blinded quality and readability analysis of answers across cartilage tissue engineering and cartilage repair surgery domains. Authored by Sen Yang Xiao and shared under a CC-BY-4.0 license on figshare.
A study comparing the performance of ChatGPT (GPT-4), DeepSeek (V3), and Google Search on cartilage repair questions. The dataset includes 20 frequently asked questions from cartilage tissue engineering (2023) and cartilage repair surgery (2024) domains, with answers evaluated for accuracy, safety, and readability. Authored by Sen Yang Xiao and last updated on 2026-05-29.
A 2026 study by Sen Yang Xiao compares ChatGPT, DeepSeek, and Google Search on cartilage repair questions. The dataset contains results from a matched three-way comparison across cartilage tissue engineering (2023) and cartilage repair surgery (2024) domains. It includes blinded quality scores for accuracy, safety, and hallucination, as well as readability assessments.
A 20.8 KB study compares ChatGPT, DeepSeek, and Google Search on cartilage repair questions. The analysis uses a dual-axis framework integrating classification, blinded quality scoring, and readability assessment. Author Sen Yang Xiao published the results under a CC-BY-4.0 license in May 2026.
Table 13_AI dialogues in cartilage repair compares ChatGPT, DeepSeek, and Google Search on cartilage repair questions. The dataset contains results from a matched three-way comparison across cartilage tissue engineering (2023) and cartilage repair surgery (2024) domains, including blinded quality scoring and readability analysis. The dataset was authored by Sen Yang Xiao, last updated on 2026-05-29, and is shared under a CC-BY-4.0 license.
A comparative study evaluates ChatGPT, DeepSeek, and Google Search responses to cartilage repair questions. The dataset likely contains question classifications, quality scores, and readability metrics from a dual-axis framework. The author is Sen Yang Xiao, and the data was last updated on May 29, 2026.
A study comparing ChatGPT, DeepSeek, and Google Search on 20 cartilage repair questions across tissue engineering and surgery domains. The dataset includes blinded quality scoring using the Accuracy-Safety-Hallucination framework and readability analysis via the Flesch-Kincaid formula. Authored by Sen Yang Xiao and last updated on 2026-05-29 under a CC-BY-4.0 license.
A matched three-way comparison of ChatGPT, DeepSeek, and Google Search on cartilage repair questions. The dataset includes blinded quality scores and readability analysis for 20 questions across cartilage tissue engineering and surgery domains. Sen Yang Xiao published the results on figshare in 2026.
Table 10_AI dialogues in cartilage repair: which guides evidence-based decisions better? is a dataset by Sen Yang Xiao, last updated on 2026-05-29. It contains results from a study comparing ChatGPT, DeepSeek, and Google Search on cartilage repair questions across tissue engineering and surgery domains. The dataset includes matched comparisons, blinded quality scoring using the ASH framework, and readability analysis.
29 Systemic Lupus Erythematosus (SLE) and 29 Sjögren's disease (SjD) patients, plus 37 healthy controls, were monitored for 6 months using wrist-worn actigraphy and a non-contact radio wave sleep sensor. The dataset, authored by Mehdi Boukhechba and shared under CC-BY-4.0, contains digital measures of physical activity, sleep staging, and breathing signals, alongside self-reported eDiaries and clinical assessments. Results indicate lower physical activity in both patient groups and impaired sleep/breathing in SLE patients compared to controls.
Rui Gu's research dataset on figshare, last updated 2026-05-29, investigates biomarkers for estimating the time since traumatic brain injury. The data likely contains gene expression levels for FDX1, DLD, SLC31A1, and LIAS from human postmortem and mouse model samples. Findings suggest these markers correlate with injury progression within 30 days post-TBI.
Retrospective data from 2,610 patients who underwent percutaneous coronary intervention (PCI), collected by Feilong Shao. The dataset examines the association between the C-reactive protein-triglyceride-glucose index (CTI) and major adverse cardiovascular events (MACEs), with 459 events recorded during follow-up. The study was last updated in May 2026.
A retrospective study of 232 elderly patients with single-level lumbar spinal stenosis, comparing 45 patients with diabetes mellitus to 187 without. Propensity score matching created a balanced cohort of 74 patients for analysis over a 60–108 month follow-up period. The dataset includes demographic data, clinical scores, radiographic parameters, complications, and reoperation rates.
A retrospective study of 232 elderly patients with single-level lumbar spinal stenosis who underwent percutaneous transforaminal endoscopic decompression, including 45 patients with diabetes mellitus. The dataset contains demographic data, clinical scores, radiographic parameters, complications, and reoperation rates, with a follow-up duration of 60–108 months. It was created by Tusheng Li and shared under a CC-BY-4.0 license.