llms Search Results


llms  (Reddit Inc)
86
Reddit Inc llms
Llms, supplied by Reddit Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/pmc12894197-45-6-13?v=Reddit+Inc
Average 86 stars, based on 1 article reviews
llms - by Bioz Stars, 2026-07
86/100 stars
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clinicalspecific llms meditron  (Meditron GmbH)
90
Meditron GmbH clinicalspecific llms meditron
Clinicalspecific Llms Meditron, supplied by Meditron GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/pm40055634-217-14-18?v=Meditron+GmbH
Average 90 stars, based on 1 article reviews
clinicalspecific llms meditron - by Bioz Stars, 2026-07
90/100 stars
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llms  (McNichols Co)
90
McNichols Co llms
Llms, supplied by McNichols Co, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/10__1016_slash_j__caeai__2024__100338-83-8-2?v=McNichols+Co
Average 90 stars, based on 1 article reviews
llms - by Bioz Stars, 2026-07
90/100 stars
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llms  (InterPro Inc)
90
InterPro Inc llms
Llms, supplied by InterPro Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/pmc11701551-257-4-20?v=InterPro+Inc
Average 90 stars, based on 1 article reviews
llms - by Bioz Stars, 2026-07
90/100 stars
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llms  (Wulff labs)
90
Wulff labs llms
Llms, supplied by Wulff labs, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/pm39147947-231-38-69?v=Wulff+labs
Average 90 stars, based on 1 article reviews
llms - by Bioz Stars, 2026-07
90/100 stars
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llms gpt davinci  (DaVinci Biosciences)
90
DaVinci Biosciences llms gpt davinci
Llms Gpt Davinci, supplied by DaVinci Biosciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/10__1609_slash_aiide__v20i1__31867-115-13-14?v=DaVinci+Biosciences
Average 90 stars, based on 1 article reviews
llms gpt davinci - by Bioz Stars, 2026-07
90/100 stars
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llms gpt-4  (DaVinci Biosciences)
90
DaVinci Biosciences llms gpt-4
Llms Gpt 4, supplied by DaVinci Biosciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/10__1145_slash_3641289-443-22-27?v=DaVinci+Biosciences
Average 90 stars, based on 1 article reviews
llms gpt-4 - by Bioz Stars, 2026-07
90/100 stars
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llms  (CHOWDHURY AND CO LUTON LIMITED)
90
CHOWDHURY AND CO LUTON LIMITED llms
Llms, supplied by CHOWDHURY AND CO LUTON LIMITED, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/10__1080_slash_08839514__2024__2414483-69-8-4?v=CHOWDHURY+AND+CO+LUTON+LIMITED
Average 90 stars, based on 1 article reviews
llms - by Bioz Stars, 2026-07
90/100 stars
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open source medical llms meditron llm  (Meditron GmbH)
90
Meditron GmbH open source medical llms meditron llm
Open Source Medical Llms Meditron Llm, supplied by Meditron GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/10__47941_slash_ijce__2374-2-8-14?v=Meditron+GmbH
Average 90 stars, based on 1 article reviews
open source medical llms meditron llm - by Bioz Stars, 2026-07
90/100 stars
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llms meditron 70b  (Meditron GmbH)
90
Meditron GmbH llms meditron 70b
The IORRA cohort (n = 15,004) was used for this study, with patients who had a follow-up period of less than two years excluded (n = 3,139). The remaining data were divided into three sets: a training set (n = 9,500), a validation set (n = 102), and a test set (n = 2,263). <t>Llama2</t> and Meditron served as the large language models (LLMs) for predicting disease activity and physical function. Meditron, pre-trained on medical literature, were fine-tuned using the data of training set. A linear regression was used for comparison. Predictive indicators were DAS28-ESR, DAS28-CRP, and CDAI (high disease activity or remission), and J-HAQ score (high score or remission) after two years.
Llms Meditron 70b, supplied by Meditron GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/med_rxiv__2024__10__14__24315448-38-7-10?v=Meditron+GmbH
Average 90 stars, based on 1 article reviews
llms meditron 70b - by Bioz Stars, 2026-07
90/100 stars
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domain-pretrained llms meditron  (Meditron GmbH)
90
Meditron GmbH domain-pretrained llms meditron
The IORRA cohort (n = 15,004) was used for this study, with patients who had a follow-up period of less than two years excluded (n = 3,139). The remaining data were divided into three sets: a training set (n = 9,500), a validation set (n = 102), and a test set (n = 2,263). <t>Llama2</t> and Meditron served as the large language models (LLMs) for predicting disease activity and physical function. Meditron, pre-trained on medical literature, were fine-tuned using the data of training set. A linear regression was used for comparison. Predictive indicators were DAS28-ESR, DAS28-CRP, and CDAI (high disease activity or remission), and J-HAQ score (high score or remission) after two years.
Domain Pretrained Llms Meditron, supplied by Meditron GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/pmc11701117__42004_2024_1394_MOESM1_ESM-86-24-27?v=Meditron+GmbH
Average 90 stars, based on 1 article reviews
domain-pretrained llms meditron - by Bioz Stars, 2026-07
90/100 stars
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llms  (InHealthcare)
90
InHealthcare llms
The IORRA cohort (n = 15,004) was used for this study, with patients who had a follow-up period of less than two years excluded (n = 3,139). The remaining data were divided into three sets: a training set (n = 9,500), a validation set (n = 102), and a test set (n = 2,263). <t>Llama2</t> and Meditron served as the large language models (LLMs) for predicting disease activity and physical function. Meditron, pre-trained on medical literature, were fine-tuned using the data of training set. A linear regression was used for comparison. Predictive indicators were DAS28-ESR, DAS28-CRP, and CDAI (high disease activity or remission), and J-HAQ score (high score or remission) after two years.
Llms, supplied by InHealthcare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/llms/pm39828800-167-6-7?v=InHealthcare
Average 90 stars, based on 1 article reviews
llms - by Bioz Stars, 2026-07
90/100 stars
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Image Search Results


The IORRA cohort (n = 15,004) was used for this study, with patients who had a follow-up period of less than two years excluded (n = 3,139). The remaining data were divided into three sets: a training set (n = 9,500), a validation set (n = 102), and a test set (n = 2,263). Llama2 and Meditron served as the large language models (LLMs) for predicting disease activity and physical function. Meditron, pre-trained on medical literature, were fine-tuned using the data of training set. A linear regression was used for comparison. Predictive indicators were DAS28-ESR, DAS28-CRP, and CDAI (high disease activity or remission), and J-HAQ score (high score or remission) after two years.

Journal: medRxiv

Article Title: Fine-tuning and pre-training improve the predictive accuracy of large language models for rheumatoid arthritis disease activity

doi: 10.1101/2024.10.14.24315448

Figure Lengend Snippet: The IORRA cohort (n = 15,004) was used for this study, with patients who had a follow-up period of less than two years excluded (n = 3,139). The remaining data were divided into three sets: a training set (n = 9,500), a validation set (n = 102), and a test set (n = 2,263). Llama2 and Meditron served as the large language models (LLMs) for predicting disease activity and physical function. Meditron, pre-trained on medical literature, were fine-tuned using the data of training set. A linear regression was used for comparison. Predictive indicators were DAS28-ESR, DAS28-CRP, and CDAI (high disease activity or remission), and J-HAQ score (high score or remission) after two years.

Article Snippet: For this study, we utilized two LLMs: Llama2 (70B) and Meditron (70B).

Techniques: Biomarker Discovery, Activity Assay, Comparison

Receiver operating characteristic (ROC) curves displaying the classification performance of five models in predicting high disease activity and remission across three disease activity indices and high score and remission in J-HAQ in patients with rheumatoid arthritis: DAS28-ESR (A), DAS28-CRP (B), CDAI (C), and J-HAQ (D). The models evaluated include a linear regression model, Llama2 without fine-tuning, Llama2 with fine-tuning, Meditron without fine-tuning, and Meditron with fine-tuning. For DAS28-ESR (A), the ROC curves on the left show the models’ performance in predicting high disease activity (DAS28-ESR >5.1), while the curves on the right assess the models’ performance in predicting remission (DAS28-ESR <2.6). A similar layout is followed for DAS28-CRP (>4.1 for high disease activity and <2.3 for remission) (B), and CDAI (>22 for high disease activity and ≤2.8 for remission) (C). For J-HAQ (D), the left and right panel show the models’ performance in predicting J-HAQ score >2.5 and J-HAQ <0.5, respectively.

Journal: medRxiv

Article Title: Fine-tuning and pre-training improve the predictive accuracy of large language models for rheumatoid arthritis disease activity

doi: 10.1101/2024.10.14.24315448

Figure Lengend Snippet: Receiver operating characteristic (ROC) curves displaying the classification performance of five models in predicting high disease activity and remission across three disease activity indices and high score and remission in J-HAQ in patients with rheumatoid arthritis: DAS28-ESR (A), DAS28-CRP (B), CDAI (C), and J-HAQ (D). The models evaluated include a linear regression model, Llama2 without fine-tuning, Llama2 with fine-tuning, Meditron without fine-tuning, and Meditron with fine-tuning. For DAS28-ESR (A), the ROC curves on the left show the models’ performance in predicting high disease activity (DAS28-ESR >5.1), while the curves on the right assess the models’ performance in predicting remission (DAS28-ESR <2.6). A similar layout is followed for DAS28-CRP (>4.1 for high disease activity and <2.3 for remission) (B), and CDAI (>22 for high disease activity and ≤2.8 for remission) (C). For J-HAQ (D), the left and right panel show the models’ performance in predicting J-HAQ score >2.5 and J-HAQ <0.5, respectively.

Article Snippet: For this study, we utilized two LLMs: Llama2 (70B) and Meditron (70B).

Techniques: Activity Assay