Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:52:44.434428Z
Paper Citation Record · LEDGER
Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM)
As of 18 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 0 inbound Pith citation observations for arXiv:2505.11738.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
3 of 3 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation edaa6160-c815-4b5c-9e20-3900801a97f4 · outbound
This paper cites & Gales, M.
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:52:44.421958Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6067c20d-78c3-4ea8-b1aa-485f289b6981 · outbound
This paper cites A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation.
Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM) A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:52:44.428929Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 655d32e2-1f6e-4b72-8b32-65642e68f59b · outbound
This paper cites The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge.
Automated Real-time Assessment of Intracranial Hemorrhage Detection AI Using an Ensembled Monitoring Model (EMM) The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:52:44.434428Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.