Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Country Primary-Outcome Gap

Which country-linked CT.gov portfolios most often leave older study pages without primary outcome descriptions, obscuring the main endpoint for public readers? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and exploded country links. We defined a primary-outcome gap as a missing primary outcome description, then ranked country-linked portfolios with at least 500 linked studies by stock and rate. The United States led the country-linked primary-outcome-gap stock table at 22,711 studies, followed by France at 4,653, Canada at 4,234, and Germany at 4,067. Iran had the sharpest large-country primary-outcome-gap rate at 28.5 percent, while France reached 27.7 percent and Norway 27.5 percent. Country-linked primary-outcome gaps show where registry pages most often omit the single line that defines the main endpoint of a study. Country-linked rows are non-exclusive because multinational studies can contribute to more than one national portfolio in registry link tables. They show registry link geography, not legal jurisdiction.

Outside Notes

Type: methods
Primary estimand: Primary-outcome-gap stock among older studies missing the primary outcome description field
App: CT.gov Country Primary-Outcome Gap dashboard
Data: 249,507 eligible older closed interventional studies with country-linked primary-outcome-gap stock and rate summaries
Code: https://github.com/mahmood726-cyber/ctgov-country-primary-outcome-gap
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.