Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Strict-Proxy Repeaters

Which sponsors still dominate the strict U.S.-nexus CT.gov core once attention shifts from sector averages to named repeaters? We analysed the 58,598-study strict U.S.-nexus drug-biological-device proxy extracted from the March 29, 2026 full-registry snapshot. We ranked strict-core sponsors with at least 50 studies and compared sponsor classes and condition families on missing-results and black-box stock inside that regulated-looking subset. National Cancer Institute carried the largest strict-core no-results stock at 361 studies, followed by Mayo Clinic at 204, MD Anderson at 198, and Sanofi at 186. OTHER and INDUSTRY remained close on strict-core stock at 8,824 and 7,983 studies, but NETWORK had the highest large-class no-results rate at 48.8 percent. The strict-core backlog therefore remains institutionally distributed across government-linked, academic, and industry sponsors rather than collapsing into one regulated archetype. These repeaters sit inside a conservative proxy core and should not be read as formal ACT or FDAAA legal determinations for specific studies or sponsors or enforcement.

Outside Notes

Type: methods
Primary estimand: Named-sponsor and sponsor-class missing-results stock inside the strict U.S.-nexus proxy core
App: CT.gov Strict-Proxy Repeaters dashboard
Data: 58,598-study strict U.S.-nexus drug-biological-device proxy extracted from the older closed interventional CT.gov universe
Code: https://github.com/mahmood726-cyber/ctgov-strict-proxy-repeaters
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.