Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Country Enrollment Gap

Which country-linked CT.gov portfolios most often leave older study pages without actual enrollment, obscuring realized sample size after study closure? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and exploded country links. We defined an enrollment gap as missing actual enrollment among older closed studies, then ranked country-linked portfolios with at least 500 linked studies by stock and rate. The United States led the stock table at 4,573 studies, followed by Canada at 797, Germany at 663, and France at 559. Iran had the highest large-country enrollment-gap rate at 6.3 percent, while Israel reached 6.1 percent and Norway 5.4 percent. Country-linked enrollment gaps show where realized sample-size discipline remains weak even after studies are old enough that timing-based excuses should be less plausible. Country-linked rows are non-exclusive because multinational studies can contribute to more than one national portfolio in the registry as they appear here today for outside readers.

Outside Notes

Type: methods
Primary estimand: Enrollment-gap stock among older studies missing actual enrollment
App: CT.gov Country Enrollment Gap dashboard
Data: 249,507 eligible older closed interventional studies with actual-enrollment gap stock and rate summaries
Code: https://github.com/mahmood726-cyber/ctgov-country-enrollment-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.