Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Enrollment-Size Gap

How much of ClinicalTrials.gov hiddenness tracks trial enrollment size once older closed interventional studies are grouped into comparable size buckets? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and binned them by recorded enrollment. The project compares two-year no-results rates, ghost-protocol rates, full visibility, and sponsor-class contrasts across enrollment buckets from 1-50 through 5,001+ participants. Studies enrolling 1 to 50 participants showed a 73.2 percent no-results rate and a 47.6 percent ghost-protocol rate. Studies enrolling 1,001 to 5,000 participants fell to 62.4 percent no results and 18.7 percent ghost protocols, while large OTHER-sponsored studies still remained highly obscured. Trial scale therefore matters, but size alone does not erase sponsor-driven reporting debt within the public registry surface. That pattern persists across tiny studies and surprisingly large nonindustry backlogs alike. Enrollment is registry-recorded and can be missing, estimated, or misclassified, so these buckets describe visible scale rather than adjudicated participant counts.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across enrollment-size buckets among eligible older CT.gov studies
App: CT.gov Enrollment-Size Gap dashboard
Data: 249,507 eligible older closed interventional studies grouped into enrollment-size buckets
Code: https://github.com/mahmood726-cyber/ctgov-enrollment-size-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.