Paper Citation Record · LEDGER

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework

As of 10 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2507.22464.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.22464 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:44:11.441992Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5dc9986c-8266-4a31-8794-07ab2eb55278 · outbound

This paper cites The global burden of chronic kidney disease: estimates, variability and pitfalls,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework The global burden of chronic kidney disease: estimates, variability and pitfalls,

Reference 1

Resolution: verified exact; doi, observed 2026-08-06T11:44:11.758159Z

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Unresolved cited work

Reference 2

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Unresolved cited work

Reference 3

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This paper cites Non-compliance could lead to a faster decline in kidney function.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Non-compliance could lead to a faster decline in kidney function

Reference 4

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Observation fada441f-f0a3-48fc-b2d2-e62b832d7bc4 · outbound

This paper cites New Equations for Estimating Glomerular Filtration Rate,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework New Equations for Estimating Glomerular Filtration Rate,

Reference 5

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Observation f6f0e66a-7d60-47b4-ab34-7c9a82fb9d24 · outbound

This paper cites Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review,

Reference 6

Resolution: verified exact; doi, observed 2026-08-06T11:44:11.713833Z

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Observation 8339b284-ce63-4ad2-91b6-bb99c47eee3d · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework On the Opportunities and Risks of Foundation Models

Reference 7

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Observation 39d7c515-cf92-4b2e-920e-68c26cef99b9 · outbound

This paper cites The future landscape of large language models in medicine,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework The future landscape of large language models in medicine,

Reference 8

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Observation 02fdbf02-f455-4c93-8893-a95dfa21fd17 · outbound

This paper cites Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models

Reference 9

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Observation 8144a952-e827-46eb-83e4-3abff877f5e6 · outbound

This paper cites Large language models in healthcare and medical domain: A review,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Large language models in healthcare and medical domain: A review,

Reference 10

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Observation c2ddf321-bd58-4595-9c58-7ae99424d62e · outbound

This paper cites Social Learning: Towards Collaborative Learning with Large Language Models.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Social Learning: Towards Collaborative Learning with Large Language Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:44:11.655193Z

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This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Chain-of-thought prompting elicits reasoning in large language models,

Reference 12

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Observation 0bbe4606-4246-45a7-a187-0defe060f4df · outbound

This paper cites Interpretation as abduction,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Interpretation as abduction,

Reference 13

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Observation 6827ff42-c4fc-4bd3-a4ba-b21bce4b3c61 · outbound

This paper cites Chronic kidney disease and the global public health agenda: an international consensus,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Chronic kidney disease and the global public health agenda: an international consensus,

Reference 14

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Observation 5e671e9d-261c-4e71-b9a9-cbc346c9ebec · outbound

This paper cites KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease,

Reference 15

Resolution: verified exact; doi, observed 2026-08-06T11:44:11.606378Z

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Observation f7f327a0-28d0-4f68-9725-12b90d8b7276 · outbound

This paper cites Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning,

Reference 16

Resolution: verified exact; doi, observed 2026-08-06T11:44:11.592800Z

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Observation 83e58836-733d-4e8f-bb28-bc7e7ed25589 · outbound

This paper cites Development and External Validation of a Machine Learning Model for Progression of CKD,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Development and External Validation of a Machine Learning Model for Progression of CKD,

Reference 17

Resolution: verified exact; doi, observed 2026-08-06T11:44:11.579216Z

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This paper cites A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts,

Reference 18

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework A Unified Approach to Interpreting Model Predictions

Reference 19

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This paper cites ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,

Reference 20

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This paper cites ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations.,.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations.,

Reference 21

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework TabLLM: Few -shot Classification of Tabular Data with Large Language Models,

Reference 22

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Observation 7967bfe7-5128-4c06-99c6-d4e2d08398c8 · outbound

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Gemini: A Family of Highly Capable Multimodal Models

Reference 23

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework GPT-4o System Card

Reference 24

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities

Reference 25

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework A Survey on the Memory Mechanism of Large Language Model based Agents

Reference 26

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI,

Reference 27

Resolution: verified exact; doi, observed 2026-08-06T11:44:11.495005Z

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Observation d5da26b3-aa7f-4fa1-a520-8336d6eb6992 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 28

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework The Llama 3 Herd of Models

Reference 29

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework meta-llama/Llama-3.2-11B-Vision-Instruct · Hugging Face

Reference 30

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Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework Gemma 3 Technical Report

Reference 31

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Pith citing papers

No inbound Pith citation observations are available.