Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T13:34:44.761341Z
Paper Citation Record · LEDGER
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data
As of 11 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 0 inbound Pith citation observations for arXiv:2502.07836.
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
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
5 of 5 outbound references displayed
- verified exact0
- verified fuzzy2
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 21005a15-6ba0-4794-ba3b-ffc82d13ebc1 · outbound
This paper cites Multi-omic machine learning predictor of breast cancer therapy response,.
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data Multi-omic machine learning predictor of breast cancer therapy response,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T13:34:44.801506Z
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 619cb7ee-1b82-4b66-afe2-1c2eae6c1152 · outbound
This paper cites Learning to Diagnose with LSTM Recurrent Neural Networks.
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data Learning to Diagnose with LSTM Recurrent Neural Networks
Reference 66
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T13:34:44.754911Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bfb76894-ebc2-4b9f-9317-81f718cc5d54 · outbound
This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Reference 109
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T13:34:44.757733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81737cfc-87f6-49b3-ae63-6aede2a571aa · outbound
This paper cites Cancer statistics, 2024,.
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data Cancer statistics, 2024,
Reference 141
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T13:34:44.808438Z
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 028ecc0b-a00e-4485-914d-c0177e01c623 · outbound
This paper cites DoubleMLDeep: Estimation of Causal Effects with Multimodal Data.
Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data DoubleMLDeep: Estimation of Causal Effects with Multimodal Data
Reference 154
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T13:34:44.761341Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.