Pith. sign in

Paper Citation Record · LEDGER

Enabling Scalable Evaluation of Bias Patterns in Medical LLMs

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.14763.

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

pith.paper-citation-record.v1
2410.14763 v2

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:09:13.940093Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T11:09:14.297854Z

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 9d92a9f2-ac20-4417-8de3-a324d9d79891 · inbound

ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs cites this paper.

ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs Enabling Scalable Evaluation of Bias Patterns in Medical LLMs

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-16T11:09:14.301269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-16T11:09:13.940093Z digest=sha256:fdd6b86d64b35166a0e22216e562e8dcab8f7adf9a57bfb82b142ba60d74d6fd

Observation 8ffae916-3841-4942-90ef-9d76c21439b5 · inbound

HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning cites this paper.

HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning Enabling Scalable Evaluation of Bias Patterns in Medical LLMs

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-04T14:41:59.314877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T14:41:59.314877Z digest=sha256:3073e8a152c6aab616665e96fa34d777dff103013bf21d5f64f6d6423d287fe4