Paper Citation Record · LEDGER

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.22919.

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

pith.paper-citation-record.v1
2507.22919 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:40:16.563841Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy22
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 58f24c15-1ac5-4cb3-8eb5-f500c4f0631e · outbound

This paper cites World Health Organi- zation, Geneva, 2018.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations World Health Organi- zation, Geneva, 2018

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.815033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c28f57c5-2b75-484e-a92a-a04d2bcad277 · outbound

This paper cites Food and Drug Administration.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Food and Drug Administration

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.669500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8a1a71e0-8420-443f-bfcf-b5185c03dcdf · outbound

This paper cites Clinical trials, n.d.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Clinical trials, n.d

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.436376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4eb31850-6d52-4bd8-83a1-ed2fccea8c5d · outbound

This paper cites basic results.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations basic results

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.295495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf9abc6e-5df6-49bd-93b2-01c835043902 · outbound

This paper cites Reporting summary results in clinical trial registries: updated guidance from who.The Lancet Global Health, 13(4):e759–e768, 2025.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Reporting summary results in clinical trial registries: updated guidance from who.The Lancet Global Health, 13(4):e759–e768, 2025

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:20.083783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 85b11416-05dd-4d98-a9e7-29b9118d0da9 · outbound

This paper cites Clinical trial registration was associated with lower risk of bias compared with non-registered trials among trials included in systematic reviews.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Clinical trial registration was associated with lower risk of bias compared with non-registered trials among trials included in systematic reviews

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.890068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 626d1284-c3e4-4c65-8026-d4cef2c6ba6f · outbound

This paper cites Better access to information about clinical trials.Annals of Internal Medicine, 133(8):609–614, 2000.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Better access to information about clinical trials.Annals of Internal Medicine, 133(8):609–614, 2000

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.706243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d3642cd1-2156-4a72-b672-93487cf13efa · outbound

This paper cites The clinicaltrials.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations The clinicaltrials

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.443259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fc21d49c-33bd-45e9-99ef-eb04ba84a12f · outbound

This paper cites Department of Health and Human Services.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Department of Health and Human Services

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.241196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dd51c764-7453-40ff-a485-6e18b3ae41a2 · outbound

This paper cites Predictive modeling of clinical trial terminations using feature engineering and embedding learning.Scientific reports, 11(1):3446, 2021.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Predictive modeling of clinical trial terminations using feature engineering and embedding learning.Scientific reports, 11(1):3446, 2021

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:19.052603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6719527a-93c4-4546-8d9e-f1a33fa9a09e · outbound

This paper cites Predicting publication of clinical trials using structured and unstructured data: model development and validation study.Journal of Medical Internet Research, 24(12):e38859, 2022.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Predicting publication of clinical trials using structured and unstructured data: model development and validation study.Journal of Medical Internet Research, 24(12):e38859, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.886436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9347196-f636-4d07-bbb2-0b07e433fdd2 · outbound

This paper cites Key indicators of phase transition for clinical trials through machine learning.Drug discovery today, 25(2):414–421, 2020.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Key indicators of phase transition for clinical trials through machine learning.Drug discovery today, 25(2):414–421, 2020

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.729733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e2bfe42d-b44e-4c5f-bc9f-a16591dbf0e5 · outbound

This paper cites Predicting phase 1 lymphoma clinical trial durations using machine learning: An in-depth analysis and broad application insights.Clinics and Practice, 14(1):69–88, 2023.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Predicting phase 1 lymphoma clinical trial durations using machine learning: An in-depth analysis and broad application insights.Clinics and Practice, 14(1):69–88, 2023

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.545113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8f59cbf-919d-49b3-93b5-7a5dc3fe703f · outbound

This paper cites Synthetic and external controls in clinical trials–a primer for researchers.Clinical epidemiology, pages 457–467, 2020.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Synthetic and external controls in clinical trials–a primer for researchers.Clinical epidemiology, pages 457–467, 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.377850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cda887a-329c-4227-b310-b7a84e1ee78a · outbound

This paper cites Table meets llm: Can large language models understand structured table data? a benchmark and empirical study.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Table meets llm: Can large language models understand structured table data? a benchmark and empirical study

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:18.225310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c4fb6615-164b-451d-b308-80a155263e3c · outbound

This paper cites Turl: Table understanding through representation learning.ACM SIGMOD Record, 51(1):33–40, 2022.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Turl: Table understanding through representation learning.ACM SIGMOD Record, 51(1):33–40, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:14.742264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c21d5137-988d-44ff-a833-ee2de15930d9 · outbound

This paper cites an unresolved cited work.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:40:18.103086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5256f605-5db4-4115-b7b2-16a6676ac273 · outbound

This paper cites Biobert: a pre-trained biomedical language representation model for biomedical text mining.Bioinformatics, 36 (4):1234–1240, 2020.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Biobert: a pre-trained biomedical language representation model for biomedical text mining.Bioinformatics, 36 (4):1234–1240, 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.932099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bcf2f6f4-740f-410e-a6d5-965eb1040440 · outbound

This paper cites Publicly Available Clinical BERT Embeddings.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Publicly Available Clinical BERT Embeddings

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:15.176396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f0ae0c7-a080-4d74-b776-fe4c34a4a8cf · outbound

This paper cites Clinicalt5: A generative language model for clinical text.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Clinicalt5: A generative language model for clinical text

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.784213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e24ca660-0b5f-4c93-b958-9a4fd3e6fb52 · outbound

This paper cites M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, 2024.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations M3-embedding: Multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, 2024

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.631363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 368920ac-5efd-4ca0-a960-9d8fadb844fb · outbound

This paper cites Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:15.556901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 504d0504-6f59-4adc-9f5c-f9afde68be8e · outbound

This paper cites Longformer: The Long-Document Transformer.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Longformer: The Long-Document Transformer

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:15.659938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29ef4dca-95da-46a1-ae81-b988de40fe41 · outbound

This paper cites Xlnet: Generalized autoregressive pretraining for language understanding.Advances in neural information processing systems, 32, 2019.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Xlnet: Generalized autoregressive pretraining for language understanding.Advances in neural information processing systems, 32, 2019

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:15.709649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5df8d9c5-10de-4dd6-b78c-028e400c8648 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:15.813486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ea4d9fb-1bda-4191-a2c3-230ae51a305f · outbound

This paper cites Found in the middle: How language models use long contexts better via plug-and-play positional encoding.Advances in Neural Information Processing Systems, 37:60755–60775, 2024.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Found in the middle: How language models use long contexts better via plug-and-play positional encoding.Advances in Neural Information Processing Systems, 37:60755–60775, 2024

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.444769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a68f023e-6aa6-447e-8423-0aa2e0c2dd20 · outbound

This paper cites Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:15.987065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08754ccf-105b-427b-8665-82e2b405e837 · outbound

This paper cites Transfer Learning with Clinical Concept Embeddings from Large Language Models.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Transfer Learning with Clinical Concept Embeddings from Large Language Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:40:16.796555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9a23e391-69c1-44dd-96a7-69794e2f9eac · outbound

This paper cites Decoupled Weight Decay Regularization.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Decoupled Weight Decay Regularization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:16.143934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0356ad2-2ea8-4264-9983-22fa47214cb3 · outbound

This paper cites National Academies Press Washington, DC, 2007.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations National Academies Press Washington, DC, 2007

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.315631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 01e5183d-6e13-4329-8ced-4db581587ea5 · outbound

This paper cites Postmarketing adverse drug reactions: A duty to report?Neurology: Clinical Practice, 3(4):288–294, 2013.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Postmarketing adverse drug reactions: A duty to report?Neurology: Clinical Practice, 3(4):288–294, 2013

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.126115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b9048cb-f5fb-4e9a-a61f-c40cdd3eb5c4 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:16.404878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a077027-0c4e-43ae-b852-86e1a29dc1bf · outbound

This paper cites why should i trust you?.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations why should i trust you?

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T15:40:16.503581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b441b6e8-d35f-4b20-bec6-679b22b4721f · outbound

This paper cites Large language models are zero-shot time series forecasters.Advances in Neural Information Processing Systems, 36:19622–19635, 2023.

A novel language model for predicting serious adverse event results in clinical trials from their prospective registrations Large language models are zero-shot time series forecasters.Advances in Neural Information Processing Systems, 36:19622–19635, 2023

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:40:17.000348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.