Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Country Reporting Map

Which named countries are attached to the quietest older CT.gov studies once country involvement is extracted from recorded trial locations? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and merged named country lists from the raw locations module. The project explodes country involvement at the study-country level and compares two-year no-results rates, ghost-protocol rates, and visible shares across countries with at least 800 eligible older studies. United States appears in the largest stock at 104,882 eligible older studies and shows a 52.1 percent no-results rate. Egypt is the worst large named country at 95.8 percent no results, China reaches 81.7 percent, while Poland falls to 33.5 percent and Australia to 43.4 percent. Named-country involvement therefore exposes large geographic transparency divides that are hidden by simple country-count buckets alone for large country-linked backlogs. Country labels reflect recorded study locations rather than verified enrollment shares, coordination centers, or country-specific legal duties.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across named country involvements among eligible older CT.gov studies
App: CT.gov Country Reporting Map dashboard
Data: 249,507 eligible older closed interventional studies merged with extracted named-country labels from raw locations
Code: https://github.com/mahmood726-cyber/ctgov-country-reporting-map
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.