Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-25T03:09:57.872792Z
Paper Citation Record · LEDGER
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies
As of 11 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2605.23246.
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 2026-08-11T06:34:44.6726+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
22 of 22 outbound references displayed
- verified exact14
- verified fuzzy8
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 3ddcb0b3-38c0-4511-a774-01f27d19b94d · outbound
This paper cites Smith1, Tala Fakhouri2, Run Zhuang1, Jonathan R.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Smith1, Tala Fakhouri2, Run Zhuang1, Jonathan R
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.377457Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b42ac21e-bee5-4451-858b-4e5c16ec87b8 · outbound
This paper cites Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.381290Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7772b67d-9fbd-4f62-813b-418e2d39aa5e · outbound
This paper cites an unresolved cited work.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Unresolved cited work
Reference 3
- Resolution
- verified exact
- doi, observed 2026-05-25T03:10:15.378303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a15feb19-a6a2-4f2c-b41c-29dd698b6987 · outbound
This paper cites Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.373845Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a3a564fa-fa3e-4aff-851c-7175bf5c08d7 · outbound
This paper cites an unresolved cited work.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Unresolved cited work
Reference 5
- Resolution
- verified exact
- doi, observed 2026-05-25T03:10:15.385695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 91dfcce3-77ac-43f3-b24b-0ed32332c0b8 · outbound
This paper cites The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.358418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation bbe0ba44-085a-4c15-8e29-233292319321 · outbound
This paper cites an unresolved cited work.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Unresolved cited work
Reference 7
- Resolution
- verified exact
- doi, observed 2026-05-25T03:10:15.392904Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5c9b37c6-fd02-4e31-a524-4873d8018033 · outbound
This paper cites Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.370222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 0d1f3e51-4699-4df8-bd01-d4051fae148d · outbound
This paper cites Enhancing Longitudinal Clinical Trial Efficiency with Digital Twins and Prognostic Covariate-Adjusted Mixed Models for Repeated Measures (PROCOVA-MMRM).
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Enhancing Longitudinal Clinical Trial Efficiency with Digital Twins and Prognostic Covariate-Adjusted Mixed Models for Repeated Measures (PROCOVA-MMRM)
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:10:16.850970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation eec109fe-1719-4fad-95bd-331871ec3f89 · outbound
This paper cites Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:10:16.855952Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation c3c657c3-b019-48d9-8164-219b5f5a1d98 · outbound
This paper cites Prognostic Covariate Adjustment for Binary Outcomes Using Stratification.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Prognostic Covariate Adjustment for Binary Outcomes Using Stratification
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:10:16.859983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 20eebc6e-f4fb-4d4e-b80b-50749fc264f5 · outbound
This paper cites Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:10:16.837667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 33bbc935-e940-4ce5-874e-43aa02dbca5c · outbound
This paper cites Bayesian prognostic covariate adjustment.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Bayesian prognostic covariate adjustment
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:10:16.842105Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation fc4b0294-3835-4eb0-94cd-442b5a5ea757 · outbound
This paper cites Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Bayesian Prognostic Covariate Adjustment With Additive Mixture Priors
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:10:16.846087Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4b33a2d5-92bb-4ba8-897e-c9a0e229cb42 · outbound
This paper cites Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.366452Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1333d114-2e24-4bbd-bb64-32af1375f72c · outbound
This paper cites Using AI-generated digital twins to boost clinical trial efficiency in Alzheimer's disease.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Using AI-generated digital twins to boost clinical trial efficiency in Alzheimer's disease
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.362120Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b1e10695-3da6-4e24-82a8-07941bb5eca6 · outbound
This paper cites an unresolved cited work.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Unresolved cited work
Reference 17
- Resolution
- verified exact
- doi, observed 2026-05-25T03:10:15.382258Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1b04150b-664e-4cce-b728-f400e53deec3 · outbound
This paper cites Tilavonemab in early Alzheimer's disease: results from a phase 2, randomized, double-blind study.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Tilavonemab in early Alzheimer's disease: results from a phase 2, randomized, double-blind study
Reference 18
- Resolution
- verified exact
- doi, observed 2026-05-25T03:10:15.370479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f668b268-882d-49ae-af53-1097625776eb · outbound
This paper cites Docosahexaenoic Acid Supplementation and Cognitive Decline in Alzheimer Disease: A Randomized Trial.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies Docosahexaenoic Acid Supplementation and Cognitive Decline in Alzheimer Disease: A Randomized Trial
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T03:10:15.401381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 02a06918-7acb-4a7c-a14b-0d4e9a56174f · outbound
This paper cites A randomized, double-blind, placebo-controlled trial of resveratrol for Alzheimer disease.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies A randomized, double-blind, placebo-controlled trial of resveratrol for Alzheimer disease
Reference 20
- Resolution
- verified exact
- doi, observed 2026-05-25T03:10:15.404886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 79e2e074-7690-4c41-aabf-83993c50a862 · outbound
This paper cites The ADCS valproate neuroprotection trial: Primary efficacy and safety results.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies The ADCS valproate neuroprotection trial: Primary efficacy and safety results
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-25T03:10:17.385022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7b3cda7c-49d4-47d0-8b2f-b69ba10fdcb1 · outbound
This paper cites if my study is powered to 90%, how problematic is a 3.2% power decrease?.
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies if my study is powered to 90%, how problematic is a 3.2% power decrease?
Reference 22
- Resolution
- verified exact
- doi, observed 2026-05-25T03:10:15.389883Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Pith citing papers
No inbound Pith citation observations are available.