Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Black-Box Sponsor Repeaters

Which named sponsors dominate the CT.gov black-box subset where older studies have no results, no linked paper, and no detailed description? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot. Using the wave-nine sponsor watchlist, we ranked named sponsors by black-box stock and black-box rate, then compared that table with no-results and ghost counts. Boehringer Ingelheim carried the largest named black-box stock at 755 studies, followed by GlaxoSmithKline at 579 and Pfizer at 539. Bayer was the sharper large-sponsor outlier on rate at 48.1 percent, while several top black-box repeaters were industry portfolios with hundreds of missing-results studies. The named black-box table therefore makes the industry deep-silence problem much more visible than the broader sponsor stock tables do, especially across major drug-company portfolios overall. Black-box status is a registry-page visibility definition and should not be read as proof that a sponsor produced no documentation or dissemination outside linked CT.gov fields.

Outside Notes

Type: methods
Primary estimand: Black-box stock and rate among named lead sponsors in the older-study CT.gov universe
App: CT.gov Black-Box Sponsor Repeaters dashboard
Data: 249,507 eligible older closed interventional studies with named-sponsor black-box watchlists derived from the wave-nine sponsor table
Code: https://github.com/mahmood726-cyber/ctgov-black-box-sponsor-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.