Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T23:55:04.401040Z
Paper Citation Record · LEDGER
SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
As of 10 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 1 inbound Pith citation observation for arXiv:2506.22467.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T23:08:12.850654Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-28T23:12:47.054020Z
Reference resolution
2 of 2 outbound references displayed
- verified exact1
- verified fuzzy0
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation fad31ec6-6b76-44d0-bab7-5698c9c5d720 · outbound
This paper cites Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study.
SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study
Reference 4
- Resolution
- verified exact
- doi, observed 2026-08-06T23:55:04.496925Z
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 b0b11b17-3086-498a-bb5a-b90a7feb89f8 · outbound
This paper cites How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model.
SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:55:04.401040Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation a7ac3e93-d1fc-4c33-96e3-36900816a909 · inbound
LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification cites this paper.
LegSegNet: A Public Deep Learning System for Lower Extremity CT Tissue Segmentation and Quantification SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T23:12:47.055723Z
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.