Paper Citation Record · LEDGER

ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.06469.

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

pith.paper-citation-record.v1
2411.06469 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:42:12.254183Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-30T07:04:21.340739Z

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 10985473-3d42-4677-9899-29eaadacc7d4 · inbound

A foundation model for human-AI collaboration in medical literature mining cites this paper.

A foundation model for human-AI collaboration in medical literature mining ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T13:42:12.254183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd6f4db5-1008-45d8-9bb2-8800794bfde0 · inbound

Holistic Artificial Intelligence in Medicine; improved performance and explainability cites this paper.

Holistic Artificial Intelligence in Medicine; improved performance and explainability ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T21:26:16.646606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceac88cb-19d2-4e09-b80d-2a2fa80ae11d · inbound

Toward Better EHR Reasoning in LLMs: Reinforcement Learning with Expert Attention Guidance cites this paper.

Toward Better EHR Reasoning in LLMs: Reinforcement Learning with Expert Attention Guidance ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T19:02:41.573055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 332d16ab-2818-4484-889a-7d4563866912 · inbound

From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction cites this paper.

From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-09T03:06:59.023715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 85b6dbe5-24a3-4770-bf3d-eea50266dda9 · inbound

Beyond IID: How General Are Tabular Foundation Models, Really? cites this paper.

Beyond IID: How General Are Tabular Foundation Models, Really? ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Reference 125

Resolution: verified exact; local_arxiv, observed 2026-06-30T07:04:21.342249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 768967bf-cf3d-49f9-92a5-4705c829af72 · inbound

The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy cites this paper.

The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-14T06:30:16.612345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.