Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Probable ACT/FDAAA Debt

How large is the likely U.S.-nexus reporting debt once older CT.gov studies are filtered through strict and broad ACT-style sensitivity layers? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot. We created three proxy layers using recorded U.S. locations, intervention families, and phase exclusions within older closed interventional records: broad U.S. nexus, drug/bio non-phase1, and strict drug/bio/device U.S. nexus. The strict proxy contains 58,598 older studies, with 18,475 missing-results studies and a 31.5 percent no-results rate. Debt is still old inside that supposedly regulated core: mean unresolved time is 10.92 years beyond the two-year mark, and Pre-FDAAA strict-proxy cohorts remain 87.0 percent unresolved even after U.S.-nexus filtering. The regulatory backlog therefore sits far below the raw all-study rate but remains large, old, and institutionally distributed across OTHER, INDUSTRY, and government-linked slices. These layers are conservative proxies built from registry-visible fields, not formal ACT or FDAAA legal determinations or enforceability judgments.

Outside Notes

Type: methods
Primary estimand: Missing-results rate and overdue debt within conservative U.S.-nexus ACT-style proxy layers
App: CT.gov Probable ACT/FDAAA Debt dashboard
Data: 249,507 eligible older closed interventional studies filtered into broad and strict U.S.-nexus proxy layers
Code: https://github.com/mahmood726-cyber/ctgov-probable-act-fdaaa-debt
Version: 1.0.0
Validation: FULL REGISTRY RUN

References

1. ClinicalTrials.gov API v2. National Library of Medicine. Accessed March 29, 2026.
2. DeVito NJ, Bacon S, Goldacre B. Compliance with legal requirement to report clinical trial results on ClinicalTrials.gov: a cohort study. Lancet. 2020;395(10221):361-369.
3. Zarin DA, Tse T, Williams RJ, Carr S. Trial reporting in ClinicalTrials.gov. N Engl J Med. 2016;375(20):1998-2004.

AI Disclosure

This work represents a compiler-generated evidence micro-publication built from structured registry data and deterministic summary code. AI was used as a constrained coding and drafting assistant for interface generation, packaging, and prose refinement, not as an autonomous author. The analytical choices, interpretation, and final outputs were reviewed by the author, who takes responsibility for the content.