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Paper Citation Record · LEDGER

A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2203.07018.

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

pith.paper-citation-record.v1
2203.07018 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:20.190121Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:26:55.103494Z

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 5614299a-c538-4c39-94a4-3cb37a074680 · inbound

TabularQGAN: A Quantum Generative Model for Tabular Data cites this paper.

TabularQGAN: A Quantum Generative Model for Tabular Data A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T13:13:20.190121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:13:20.190121Z digest=sha256:85a89820457d78c9312f97ae68e40f922a593278c0ce5e51e8b0f962e9c02e91

Observation fba8213f-763e-4884-ad14-7a5da889685e · inbound

Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification cites this paper.

Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources

Reference 68

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:26:55.110047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T23:26:48.794758Z digest=sha256:f28e36b6ce6d4f6605fceb4881b1048260bf45e76fcdd27862309893809afda5