Paper Citation Record · LEDGER

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI

As of 22 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2604.09009.

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

pith.paper-citation-record.v1
2604.09009 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:14:54.718410Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:14:54.718410Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-11T05:15:58.239998Z

Reference resolution

15 of 15 outbound references displayed

verified exact1
verified fuzzy11
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bbca7c88-b1ea-4856-8014-f43c47d4a1da · outbound

This paper cites an unresolved cited work.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-17T06:19:11.176545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 09c1485b-56d5-4bbf-9d17-c214c58a1cfd · outbound

This paper cites Surveys highlight catastrophic forgetting and data drift as key challenges in medical AI [5].

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Surveys highlight catastrophic forgetting and data drift as key challenges in medical AI [5]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.167668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 82961a86-eed5-45ff-b0b4-61117ed6c11b · outbound

This paper cites Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:15:58.316020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9523b525-19d0-478f-9e6c-992d08756745 · outbound

This paper cites Stage 1 Per-fold feature summaries (mean, variance/std, covariance) define the reference distribution.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Stage 1 Per-fold feature summaries (mean, variance/std, covariance) define the reference distribution

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.170945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee761d99-1460-449a-9b9d-bdaf23ee8ee6 · outbound

This paper cites The combination of feature- space monitoring and uncertainty gating effectively addresses model degradation due to data drift.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI The combination of feature- space monitoring and uncertainty gating effectively addresses model degradation due to data drift

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.147869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 883532ea-fc62-4fd4-b9b2-e3772ade8c28 · outbound

This paper cites an unresolved cited work.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-17T06:19:11.164342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9d9e256-17de-4807-89fd-ca889409ea55 · outbound

This paper cites an unresolved cited work.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-05-17T06:19:11.173691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b7e17b86-fcff-477c-a24c-e79da4b3d50e · outbound

This paper cites Long-term renal outcomes of patients with non-proliferative lupus nephritis.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Long-term renal outcomes of patients with non-proliferative lupus nephritis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.179642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 10184743-23f9-4b51-8349-e82b022bd5e7 · outbound

This paper cites A distributed system improves inter- observer and ai concordance in annotating interstitial fi- brosis and tubular atrophy.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI A distributed system improves inter- observer and ai concordance in annotating interstitial fi- brosis and tubular atrophy

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.182627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7585448d-e7a6-4f42-989c-d041038d113f · outbound

This paper cites Translating ai to clinical practice: Overcoming data shift with explainability.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Translating ai to clinical practice: Overcoming data shift with explainability

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.185948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation db3a2990-c019-4bff-a464-7d38db6aa6d4 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Overcoming catastrophic forgetting in neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.154563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dcd28d35-f502-4cec-8a9b-66f3c2b73c60 · outbound

This paper cites Continual learning in medical imaging: A survey and practical anal- ysis.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Continual learning in medical imaging: A survey and practical anal- ysis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.158039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 36397260-82ed-41e9-9738-389e019150b6 · outbound

This paper cites Trustworthy clinical ai solutions: a unified review of un- certainty quantification in deep learning models for med- ical image analysis.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Trustworthy clinical ai solutions: a unified review of un- certainty quantification in deep learning models for med- ical image analysis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.189187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3d3f3dcb-e57e-4b06-bc03-5807b95ec967 · outbound

This paper cites Learning under concept drift: A re- view.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Learning under concept drift: A re- view

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.151130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f21f700a-ace0-4b70-b213-3d0db7813e46 · outbound

This paper cites Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guar- antees.

Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guar- antees

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T06:19:11.161448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 82961a86-eed5-45ff-b0b4-61117ed6c11b · inbound