Paper Citation Record · LEDGER

Generating Counterfactual Patient Timelines from Real-World Data

As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2604.02337.

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

pith.paper-citation-record.v1
2604.02337 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T11:42:42.030779Z

measured 24 of 24 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 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

24 of 24 outbound references displayed

verified exact6
verified fuzzy17
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 684ee90d-d0c3-4607-bcca-4b57f066279f · outbound

This paper cites However, it remains challenging due to methodological limitations.

Generating Counterfactual Patient Timelines from Real-World Data However, it remains challenging due to methodological limitations

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.077475Z

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 da3e59ad-e295-4dca-9eff-1c0fcf3aacbc · outbound

This paper cites We then examined how simulated outcomes changed in response to these modifications.

Generating Counterfactual Patient Timelines from Real-World Data We then examined how simulated outcomes changed in response to these modifications

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.055792Z

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 ef72de80-d1e6-401d-bd87-80ff5c74da9e · outbound

This paper cites Records from 2023 were held out and exclusively used for counterfactual simulation.

Generating Counterfactual Patient Timelines from Real-World Data Records from 2023 were held out and exclusively used for counterfactual simulation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.057738Z

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 ae320acb-7b8c-47e8-9fcd-c25ef31eceb6 · outbound

This paper cites It also spans diverse clinical contexts and is supported by extensive evidence generated during the global pandemic.13.

Generating Counterfactual Patient Timelines from Real-World Data It also spans diverse clinical contexts and is supported by extensive evidence generated during the global pandemic.13

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.049335Z

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 5d9a797e-8580-43b3-b99a-256a6a4f3a55 · outbound

This paper cites Moonshot Project Goal-7.

Generating Counterfactual Patient Timelines from Real-World Data Moonshot Project Goal-7

Reference 5

Resolution: malformed identifier; raw_fallback, observed 2026-05-16T11:42:49.061771Z

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 dc2545e4-58c3-4e8d-952d-52c8526c8867 · outbound

This paper cites The clinical potential of counterfactual AI models.

Generating Counterfactual Patient Timelines from Real-World Data The clinical potential of counterfactual AI models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.075431Z

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 3af9dc39-5ecd-433f-8e98-19f7f2f34d25 · outbound

This paper cites Causal inference and counterfactual prediction in machine learning for actionable healthcare.

Generating Counterfactual Patient Timelines from Real-World Data Causal inference and counterfactual prediction in machine learning for actionable healthcare

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.071630Z

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 975c6287-7c2e-43d8-8f9c-05f012e6406f · outbound

This paper cites In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome.

Generating Counterfactual Patient Timelines from Real-World Data In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.051583Z

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 f95c3870-fc2b-4410-8a26-62790de90d0c · outbound

This paper cites Digital twins for health: a scoping review.

Generating Counterfactual Patient Timelines from Real-World Data Digital twins for health: a scoping review

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.059755Z

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 18392444-0ab3-4d84-8a65-bee32f2afd29 · outbound

This paper cites Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study.

Generating Counterfactual Patient Timelines from Real-World Data Foresight—a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.065905Z

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 349de187-c77b-41de-8272-12fd33bfc8e1 · outbound

This paper cites Zero shot health trajectory prediction using transformer.

Generating Counterfactual Patient Timelines from Real-World Data Zero shot health trajectory prediction using transformer

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.069763Z

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 63000cca-d836-46fc-b6be-25fc199e5246 · outbound

This paper cites Attention Is All You Need.

Generating Counterfactual Patient Timelines from Real-World Data Attention Is All You Need

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-16T11:42:48.837020Z

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 da1dae85-7341-41a6-8240-25a9d94e86ca · outbound

This paper cites Language Models are Few-Shot Learners.

Generating Counterfactual Patient Timelines from Real-World Data Language Models are Few-Shot Learners

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-16T11:42:48.825560Z

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 b6f5c7d6-9040-417d-b8b8-43b372e8aca1 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Generating Counterfactual Patient Timelines from Real-World Data Gaussian Error Linear Units (GELUs)

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-16T11:42:48.833302Z

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 e366ea11-76ba-4985-93d7-1022035248a3 · outbound

This paper cites Language models are unsupervised multitask learners.

Generating Counterfactual Patient Timelines from Real-World Data Language models are unsupervised multitask learners

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.081446Z

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 28da5929-a681-45b3-b21a-6313b0151d1d · outbound

This paper cites Decoupled Weight Decay Regularization.

Generating Counterfactual Patient Timelines from Real-World Data Decoupled Weight Decay Regularization

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-16T11:42:48.821581Z

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 717bfcd9-7600-424e-93db-b4475dae446a · outbound

This paper cites Infectious Diseases Society of America Guidelines on the Treatment and Management of Patients With COVID-19 (September 2022).

Generating Counterfactual Patient Timelines from Real-World Data Infectious Diseases Society of America Guidelines on the Treatment and Management of Patients With COVID-19 (September 2022)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.063854Z

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 d9e1594d-7aba-4c8d-947e-142a96c067e7 · outbound

This paper cites Efficiently Scaling Transformer Inference.

Generating Counterfactual Patient Timelines from Real-World Data Efficiently Scaling Transformer Inference

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:42:48.829434Z

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 0818ada6-54e0-4ac5-bb34-a611ce3dc632 · outbound

This paper cites Association of C-reactive protein with mortality in Covid-19 patients: a secondary analysis of a cohort study.

Generating Counterfactual Patient Timelines from Real-World Data Association of C-reactive protein with mortality in Covid-19 patients: a secondary analysis of a cohort study

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.073518Z

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 9a11477c-4abd-4047-8f3d-27f4c85efb1e · outbound

This paper cites Kidney disease is associated with in-hospital death of patients with COVID-19.

Generating Counterfactual Patient Timelines from Real-World Data Kidney disease is associated with in-hospital death of patients with COVID-19

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.079555Z

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 7b137822-07d0-4ec9-bab5-114253e84f6a · outbound

This paper cites COVID-19 and kidney disease: insights from epidemiology to inform clinical practice.

Generating Counterfactual Patient Timelines from Real-World Data COVID-19 and kidney disease: insights from epidemiology to inform clinical practice

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.067754Z

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 a262d979-a15d-43a2-8293-b6525f04f3c4 · outbound

This paper cites Evaluation of clinical prediction models (part 1): from development to external validation.

Generating Counterfactual Patient Timelines from Real-World Data Evaluation of clinical prediction models (part 1): from development to external validation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.044174Z

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 b9115713-6de6-4ace-a536-0e4d845e3b03 · outbound

This paper cites Individualized Treatment Effect Prediction with Machine Learning — Salient Considerations.

Generating Counterfactual Patient Timelines from Real-World Data Individualized Treatment Effect Prediction with Machine Learning — Salient Considerations

Reference 23

Resolution: verified exact; doi, observed 2026-05-16T11:42:48.784497Z

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 5ae0cc50-63b7-4355-83b3-32b4482553f0 · outbound

This paper cites The next generation of evidence-based medicine.

Generating Counterfactual Patient Timelines from Real-World Data The next generation of evidence-based medicine

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T11:42:49.053705Z

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

No inbound Pith citation observations are available.