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Paper Citation Record · LEDGER

Towards Democratization of Subspeciality Medical Expertise

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.03741.

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

pith.paper-citation-record.v1
2410.03741 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:37:07.099651Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T06:16:28.064256Z

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 a768742c-0c55-4754-a4d4-9e868b00cf9d · inbound

The Application of MATEC (Multi-AI Agent Team Care) Framework in Sepsis Care cites this paper.

The Application of MATEC (Multi-AI Agent Team Care) Framework in Sepsis Care Towards Democratization of Subspeciality Medical Expertise

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-08T17:37:07.099651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T17:37:07.099651Z digest=sha256:5fe667ab522497b68c45bf3cfa43d278e4836711ba1aec2e6a01be3d70963bd0

Observation e67e7342-26c5-4827-bb62-2a5ace367e72 · inbound

Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching cites this paper.

Conversation Forests: The Key to Fine Tuning Large Language Models for Multi-Turn Medical Conversations is Branching Towards Democratization of Subspeciality Medical Expertise

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:01:46.567450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:01:43.960781Z digest=sha256:c043026bb5aba12927f1e427bfbc110ec89c167e37c2ae6ffae29559ffd6ccfa