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Medical imaging (X-ray, CT, MRI), electronic health records, clinical trials, ECG/EEG, pathology
12,212 datasets
A 2026 multicenter cross-sectional survey of 704 ICU nurses across 32 provincial-level regions in China. The study investigates current practices and evaluation factors for removing temporary hemodialysis catheters after continuous renal replacement therapy (CRRT). Data was collected via questionnaire and analyzed using chi-square tests.
A retrospective study of 117 patients treated between August 2019 and January 2025. The dataset contains a logistic regression model and nomogram built by Chenlu Hou to predict minimal symptom expression after low-dose rituximab treatment in anti-acetylcholine receptor antibody-positive myasthenia gravis.
A retrospective study of 117 patients treated between August 2019 and January 2025, authored by Chenlu Hou. It establishes a predictive model for minimal symptom expression after low-dose rituximab treatment in anti-acetylcholine receptor antibody-positive myasthenia gravis. The model uses five clinical variables and was validated internally via bootstrap methods.
A predictive model for minimal symptom expression after low-dose rituximab treatment in anti-acetylcholine receptor antibody-positive myasthenia gravis patients. The model was developed by Chenlu Hou using logistic regression on data from 117 patients treated between August 2019 and January 2025. It incorporates five clinical variables and achieved an AUC of 0.777.
117 patient records from a retrospective study between August 2019 and January 2025 were used to build a predictive model for minimal symptom expression after low-dose rituximab treatment. Author Chenlu Hou developed a logistic regression model presented as a nomogram, achieving an AUC of 0.777. The dataset was last updated on 2026-06-03 and is shared under a CC-BY-4.0 license.
Xuan Zhang's dataset on figshare contains clinical and laboratory data from a study of 37 adolescent PCOS patients and 22 age-matched healthy controls. The data includes steroid hormone levels measured via LC-MS/MS and results from dexamethasone suppression tests performed on a subset of 24 patients. The dataset was last updated on June 3, 2026.
A prospective comparative study of 162 patients undergoing craniotomy for intracranial tumors, comparing Enhanced Recovery After Surgery (ERAS) nursing to conventional care. The dataset includes primary outcomes like length of hospital stay and secondary outcomes covering complications, recovery milestones, pain, psychological well-being, and quality of life. It was authored by Ruitong Li and last updated in June 2026.
State of Connecticut Open Expenditures - Ledger Current provides a nightly-updated record of payments made by the state government to vendors for goods and services. The dataset includes detailed transaction-level information such as vendor name, payment amount, date, department, and expense category, enabling granular tracking of public spending. Certain payee names are redacted to protect individual privacy under HIPAA and other statutes.
106 patient survey responses from a study of 221 eligible patients explore how the physical hospital environment supports physical activity after elective cardiothoracic surgery. The dataset was created by Lisenka te Lindert and last updated on 2026-06-03. It includes descriptive statistics ranking barriers and facilitators, and Spearman correlations exploring associations with participant characteristics.
A 2026 study by Marc Leon evaluates five large language models (O1, O3-mini-high, DeepSeek-R1, GPT-4, Llama3-OpenBioLLM-70B) on 15 high-fidelity cardiac surgery reasoning tasks. The dataset contains normalized performance scores across 10 evaluation dimensions, including scenario comprehension, patient safety, and hallucination avoidance, from a blinded two-phase evaluation by senior surgeons. It also records rating shifts between evaluation rounds, showing a 7.57% revision rate from affirmative to negative.
A 2026 study by Marc Leon presents a two-phase evaluation of five large language models (O1, O3-mini-high, DeepSeek-R1, GPT-4, Llama3-OpenBioLLM-70B) on 15 expert-curated cardiac surgery scenarios. The dataset contains normalized performance scores across 10 weighted evaluation dimensions, including scenario comprehension, patient safety, and hallucination avoidance. It also documents rating shifts from a blinded evaluation by senior surgeons, revealing patterns of human-AI collaboration.
Marc Leon's dataset contains results from a blinded two-phase evaluation of five large language models on 15 high-fidelity cardiac surgery scenarios. The data includes normalized performance scores across 10 weighted evaluation dimensions and records of rating revisions by senior surgeons. The dataset was last updated on 2026-05-29 and is licensed under CC-BY-4.0.
A blinded two-phase evaluation of five large language models on 15 high-fidelity cardiac surgery reasoning tasks. The dataset contains normalized performance scores across 10 weighted evaluation dimensions, including scenario comprehension and patient safety, and tracks rating revisions by senior surgeons. It was authored by Marc Leon and last updated in May 2026.
A 2026 study by Marc Leon presents a blinded two-phase evaluation of five large language models on 15 high-fidelity cardiac surgery scenarios. The dataset contains normalized performance scores for models including O1, O3-mini-high, DeepSeek-R1, GPT-4, and Llama3-OpenBioLLM-70B across 10 weighted evaluation dimensions. Results show performance variation and highlight a collaboration imbalance where clinicians over-accepted incorrect model reasoning.
A blinded two-phase evaluation of five large language models on 15 high-fidelity cardiac surgery reasoning tasks. The dataset includes normalized performance scores across 10 weighted evaluation dimensions and records of rating revisions by senior surgeons. It was authored by Marc Leon and last updated in May 2026.
Five large language models were evaluated on 15 high-fidelity cardiac surgery scenarios by senior surgeons. O1 achieved the highest median normalized score (0.896), while patient safety and hallucination avoidance were the lowest-scoring dimensions across models. The dataset, authored by Marc Leon and last updated in May 2026, documents the evaluation framework and results, concluding that LLMs are not yet ready for safe use in complex surgical settings.
A blinded two-phase evaluation assessed five large language models on 15 high-fidelity cardiac surgery reasoning tasks. Median normalized scores ranged from 0.521 to 0.896, with O1 achieving the highest score. The study, authored by Marc Leon and updated in May 2026, found that overacceptance of incorrect AI reasoning was a dominant collaboration imbalance.
15 high-fidelity cardiac surgery scenarios were used to evaluate five large language models on a 10-dimensional weighted framework. Median normalized scores ranged from 0.521 for Llama3-OpenBioLLM-70B to 0.896 for O1, with scenario comprehension scoring highest and patient safety lowest. The dataset, created by Marc Leon and published on figshare in 2026, captures a blinded two-phase evaluation where surgeons revised 7.57% of ratings from affirmative to negative after seeing reference answers.
Five large language models were evaluated on 15 high-fidelity cardiac surgery scenarios by senior surgeons using a 10-dimensional weighted framework. Median normalized scores ranged from 0.521 for Llama3-OpenBioLLM-70B to 0.896 for O1, with scenario comprehension scoring highest and patient safety lowest. The dataset, created by Marc Leon and last updated in May 2026, captures model performance and evaluator judgment shifts in a blinded two-phase study.
A research paper presents a two-phase evaluation framework for large language models in cardiac surgery. The study includes 15 high-fidelity clinical scenarios developed by senior surgeons and evaluates five LLMs using a 10-dimensional weighted framework. The dataset, a 369.8 KB PDF, was authored by Marc Leon and last updated on 2026-05-29.