Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Sponsor-Class Primary-Only Gap

Which sponsor classes most often leave older CT.gov study pages without the primary outcome description while keeping the broader detailed-description field? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and grouped them by lead sponsor class. We defined a primary-only gap as missing primary outcome description with detailed description still present, then compared sponsor-class stock, rate, and text-balance context. The Other class led sponsor-class primary-only-gap stock at 21,381 studies, followed by Industry at 7,906, NIH at 1,258, and Other Gov at 729. NIH had the highest substantive sponsor-class primary-only-gap rate at 29.4 percent, while Network reached 21.2 percent and Indiv 20.5 percent. The sponsor-class view shows that endpoint-only text gaps are not spread evenly: NIH and network portfolios are much sharper on rate, while Other and Industry dominate on stock. Sponsor classes are broad registry buckets and should be read as portfolio patterns rather than judgments about single studies.

Outside Notes

Type: methods
Primary estimand: Primary-only-gap stock among older studies missing the primary outcome description field while retaining the detailed description field
App: CT.gov Sponsor-Class Primary-Only Gap dashboard
Data: 249,507 eligible older closed interventional studies with primary-only-gap stock and rate summaries
Code: https://github.com/mahmood726-cyber/ctgov-sponsor-class-primary-only-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.