Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.01948.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:24:50.824811Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T14:24:51.806762Z
0 of 0 outbound references displayed
External citation measurements
No source-named external measurement is stored.
No outbound reference observations are available for this paper version.
Observation 7a4825d3-1e66-40d3-a024-93d598a9168b · inbound
CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
Reference 42
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17e021de-b825-49c5-9995-74a2ca43022d · inbound
CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
Reference 16
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d11fe0d5-49c2-4ddb-ad39-0f1847492e04 · inbound
CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
Reference 29
Source-reported events for the cited work
Unavailable: canonical work link unavailable.