Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.06549.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T19:41:11.123886Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T15:50:38.196467Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation dfdadb5d-338e-4f29-89f1-5ada9603466f · inbound
StaAgent: An Agentic Framework for Testing Static Analyzers cites this paper.
StaAgent: An Agentic Framework for Testing Static Analyzers In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
Reference 2024
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T15:50:38.201956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c5a0ad68-9503-4991-8d5c-5da2e8faf9dd · inbound
Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies cites this paper.
Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T19:41:11.123886Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.