Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Sponsor Excess Watchlist

Which named CT.gov sponsors remain worst once hiddenness is read as adjusted excess stock rather than raw rate alone? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot. We reused the study-mix-adjusted no-results and ghost models, then ranked sponsors with at least 100 studies by observed-versus-expected excess, black-box stock, overdue depth, and strict-core carryover. Assistance Publique - Hopitaux de Paris carried the largest adjusted excess no-results stock at 265 studies, followed by Sanofi at 197 and Cairo University at 164. Sanofi also showed 219 excess ghost protocols, while the strict U.S.-nexus core was led by National Cancer Institute at 361 missing-results studies. The sponsor backlog therefore does not collapse into one sector, but major industrial and hospital systems remain prominent repeat offenders across the international registry and its strict-core subset. These watchlists use registry-visible sponsorship and adjusted excess estimates, not audited legal responsibility, ultimate funder structure, or causally attributable silence.

Outside Notes

Type: methods
Primary estimand: Adjusted excess no-results and ghost-protocol stock among named lead sponsors with at least 100 older studies
App: CT.gov Sponsor Excess Watchlist dashboard
Data: 249,507 eligible older closed interventional studies with sponsor-level adjusted residuals and strict-core spillover fields
Code: https://github.com/mahmood726-cyber/ctgov-sponsor-excess-watchlist
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.