Paper Citation Record · LEDGER

Exploring Scaling Laws for EHR Foundation Models

As of 10 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 6 inbound Pith citation observations for arXiv:2505.22964.

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

pith.paper-citation-record.v1
2505.22964 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:04.563838Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T05:46:44.887446Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy4
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 8d7d8dde-8119-4651-9c68-e6dfb341c64c · outbound

This paper cites Training Compute-Optimal Large Language Models.

Exploring Scaling Laws for EHR Foundation Models Training Compute-Optimal Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:03.358884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73689074-02d3-47cc-8826-848f4a2f88bd · outbound

This paper cites Scaling Laws for Neural Language Models.

Exploring Scaling Laws for EHR Foundation Models Scaling Laws for Neural Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:03.481741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b04405c-212a-439a-b934-e200ba8689e6 · outbound

This paper cites GPT-4 Technical Report.

Exploring Scaling Laws for EHR Foundation Models GPT-4 Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:03.586720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d632b334-e212-4af5-a659-e8133aff924a · outbound

This paper cites Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al.

Exploring Scaling Laws for EHR Foundation Models Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:05.635607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 81e1b377-78df-4fc4-b732-853a6acc4006 · outbound

This paper cites doi: 10.1038/s41746-024-01235-0.

Exploring Scaling Laws for EHR Foundation Models doi: 10.1038/s41746-024-01235-0

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:03.816513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2b9f2f9-af66-405c-bba8-189ef67cfca6 · outbound

This paper cites an unresolved cited work.

Exploring Scaling Laws for EHR Foundation Models Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:00:05.448355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 691119cc-69ed-46f3-a544-01b41a95c989 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Exploring Scaling Laws for EHR Foundation Models RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:04.062122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d8f785d-9241-49c6-a1c2-c87e9c7f8a6d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Exploring Scaling Laws for EHR Foundation Models LLaMA: Open and Efficient Foundation Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:04.191558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae5d0401-08c4-4e8b-a692-bf0202f4a2f9 · outbound

This paper cites Attention Is All You Need.

Exploring Scaling Laws for EHR Foundation Models Attention Is All You Need

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:04.287302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90b7474e-3fc8-44e2-a853-4f586e6b3fe9 · outbound

This paper cites URL https://www.aclweb.org/ anthology/2020.emnlp-demos.6.

Exploring Scaling Laws for EHR Foundation Models URL https://www.aclweb.org/ anthology/2020.emnlp-demos.6

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:05.248249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74805f31-367a-4023-97bc-0c3d66143ce4 · outbound

This paper cites Accessed: 2025-05-16.

Exploring Scaling Laws for EHR Foundation Models Accessed: 2025-05-16

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:05.043699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e21be102-9b72-4c7b-af59-df26b4f38ee4 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Exploring Scaling Laws for EHR Foundation Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:03.148742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03433b51-0533-4f06-aa13-fd50ab345948 · outbound

This paper cites Language Models are Few-Shot Learners.

Exploring Scaling Laws for EHR Foundation Models Language Models are Few-Shot Learners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:02.790926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fbdc707-276f-4f83-a339-1ef85988dcbe · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Exploring Scaling Laws for EHR Foundation Models PaLM: Scaling Language Modeling with Pathways

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:03.055310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3133566-a3bf-43b9-a9ba-a6a3a9a5c58f · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Exploring Scaling Laws for EHR Foundation Models GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:02.692099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5aea926-174f-4a4e-9385-3afaae6ee1e7 · outbound

This paper cites The Llama 3 Herd of Models.

Exploring Scaling Laws for EHR Foundation Models The Llama 3 Herd of Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:03.235687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 694f3fda-52c3-4161-b94b-e66008892d8d · outbound

This paper cites Accessed: 2025-05-16.

Exploring Scaling Laws for EHR Foundation Models Accessed: 2025-05-16

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:00:05.883320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation d0d18516-ddd6-4ff7-877a-b0850207c5c3 · inbound

Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction cites this paper.

Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction Exploring Scaling Laws for EHR Foundation Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:23:22.779929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 51ac16ad-acca-4c1d-8542-f41751d6f3e6 · inbound

TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection cites this paper.

TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection Exploring Scaling Laws for EHR Foundation Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-10T16:45:36.671795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad2831b4-4418-4f5b-b237-eb48025c70bd · inbound

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims cites this paper.

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims Exploring Scaling Laws for EHR Foundation Models

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:15:39.606992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 388f24f6-4d8c-40c6-8aeb-601698d1949c · inbound

On the Invariance and Generality of Neural Scaling Laws cites this paper.

On the Invariance and Generality of Neural Scaling Laws Exploring Scaling Laws for EHR Foundation Models

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:15:56.052454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 269f625a-4752-4f2f-bfa4-4d8db1ea739c · inbound

Pretraining EHR Foundation Models with Patient-Aware Sampling cites this paper.

Pretraining EHR Foundation Models with Patient-Aware Sampling Exploring Scaling Laws for EHR Foundation Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T05:46:44.887446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d3cc7db-0ab0-48a0-ad54-0906663d786f · inbound

Autoregressive EHR Foundation Models with Multimodal Inputs cites this paper.

Autoregressive EHR Foundation Models with Multimodal Inputs Exploring Scaling Laws for EHR Foundation Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T05:20:34.097912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.