Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Geography-Scale Visibility

How much more visible are larger multi-site and multinational trials on ClinicalTrials.gov than single-site studies once older closed interventional records are isolated? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and grouped them by site and country footprint. The project compares two-year no-results rates, ghost-protocol rates, full visibility, and phase-specific contrasts across location and country buckets. Single-site studies showed a 79.5 percent no-results rate, whereas studies with 20 or more sites fell to 31.7 percent. Among phase III trials, single-site studies reached 76.3 percent no results while 20-plus-site trials fell to 25.7 percent on the same metric. Geography footprint therefore behaves like a strong visibility gradient rather than a decorative field count inside the registry. The gap survives even within late-phase trials that should be easiest to see. Site and country counts come from sponsor-entered location metadata and may not capture every participating site or all multinational operational detail.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across site-footprint buckets among eligible older CT.gov studies
App: CT.gov Geography-Scale Visibility dashboard
Data: 249,507 eligible older closed interventional studies grouped by site and country footprint
Code: https://github.com/mahmood726-cyber/ctgov-geography-scale-visibility
Version: 1.0.0
Validation: FULL REGISTRY RUN

References

1. ClinicalTrials.gov API v2. National Library of Medicine. Accessed March 29, 2026.
2. Zarin DA, Tse T, Williams RJ, Carr S. Trial reporting in ClinicalTrials.gov. N Engl J Med. 2016;375(20):1998-2004.
3. 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.

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.