Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Black-Box Trials

What appears when hiddenness is narrowed to black-box trials with no results, no linked publication, and no detailed description? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot. We defined a black-box trial as one with a two-year results gap, no linked publication reference, and no detailed description, then ranked sponsor classes, countries, and condition families. OTHER held the largest black-box stock at 21,375 studies, while INDUSTRY carried the highest large-class black-box rate at 23.4 percent. The United States still held 12,183 black-box studies on absolute stock, but healthy-volunteer portfolios were the sharpest condition-family extreme at 33.9 percent. The black-box view isolates a stricter silence state where industrial portfolios rate worse, while heterogeneous public and academic portfolios still dominate on count across the registry overall. Black-box status is a registry-visibility definition only and does not imply a study lacked internal documentation, external dissemination, or undiscovered reporting outside linked registry fields.

Outside Notes

Type: methods
Primary estimand: Black-box trial stock and rate among eligible older CT.gov studies
App: CT.gov Black-Box Trials dashboard
Data: 249,507 eligible older closed interventional studies with a black-box definition based on results, publication-link, and detailed-description silence
Code: https://github.com/mahmood726-cyber/ctgov-black-box-trials
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.