Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Enrollment Gap

Which condition families most often leave older CT.gov 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 using one condition-family label per study. We defined an enrollment gap as missing actual enrollment among older closed studies, then ranked large condition families by stock and rate. Oncology led the stock table at 2,765 studies, followed by the broad OTHER bucket at 1,815, Cardiovascular at 1,179, and Infectious disease at 747. Oncology also had the highest large-family enrollment-gap rate at 6.5 percent, ahead of cardiovascular at 4.5 percent and gastrointestinal and hepatic at 4.5 percent. Condition-family enrollment gaps show that realized sample-size discipline is weakest in exactly the therapeutic areas that dominate much of the older CT.gov registry stock. Condition families are keyword-derived registry groupings, so they approximate therapeutic portfolios rather than formal disease ontologies or mutually exclusive diagnoses alone.

Outside Notes

Type: methods
Primary estimand: Enrollment-gap stock among older studies missing actual enrollment
App: CT.gov Condition 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-condition-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.