Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Disease Geography Gap

How does geography reshape the hiddenness of major disease families once older CT.gov studies are grouped into U.S.-only, mixed, non-U.S., and no-country buckets? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and linked geography buckets to oncology, cardiovascular, and metabolic families. The project compares two-year no-results rates, ghost-protocol rates, visible shares, and hiddenness scores across geography buckets within each selected disease family. U.S.-plus-non-U.S. studies were the cleanest geography bucket in every disease family: 29.9 percent no results in cardiovascular, 29.4 percent in metabolic, and 39.4 percent in oncology. No-U.S. studies were worst: 89.9 percent no results in cardiovascular, 90.3 percent in metabolic, and 86.8 percent in oncology. The disease story therefore depends on geography structure, because the same clinical area can move from moderately visible to deeply hidden depending on where studies are located. Geography buckets use recorded locations rather than verified recruitment shares, sponsor domicile, or disease-burden denominators.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across geography buckets within selected disease families among eligible older CT.gov studies
App: CT.gov Disease Geography Gap dashboard
Data: 249,507 eligible older closed interventional studies linked to geography buckets and selected condition families
Code: https://github.com/mahmood726-cyber/ctgov-disease-geography-gap
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.