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Paper Citation Record · LEDGER

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1511.05942.

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

pith.paper-citation-record.v1
1511.05942 v11

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:36:40.018425Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T07:26:59.662043Z

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 6453a659-1f74-4a75-b9b2-fc57c8db5914 · inbound

Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model cites this paper.

Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-14T11:36:40.018425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:36:40.018425Z digest=sha256:ca7df1c4b96fcb2189c5a1079b4689d7b7a494ad16c0de857597b47912b87df6

Observation 0749e5ee-3154-4e4b-aeb2-676a8ef36a6f · inbound

Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models cites this paper.

Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T11:22:59.392815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:22:59.392815Z digest=sha256:d9e9e0ab7e8ab1251ea4e8ebaea3b84457469569c26879db8d6488a6cb0a371a

Observation ef60e1ab-b1a6-4e72-8667-991c07cfcef7 · inbound

Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models cites this paper.

Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-04T21:26:15.405812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-10T07:23:15.797135Z digest=sha256:91e84ff7314bb8c850bc44469449bc99b5a765b195644b3e2b325a33f7f09576