Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Sponsor Primary-Only Gap

Which named sponsors 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 ranked sponsors with at least 100 studies. We defined a primary-only gap as missing primary outcome description with detailed description still present, then compared stock, rate, and class patterns. AP-HP led the sponsor primary-only-gap stock table at 421 studies, followed by the National Cancer Institute at 402, NIAID at 351, and Sanofi at 299. Ranbaxy Laboratories Limited had the highest large-sponsor primary-only-gap rate at 98.0 percent, while Dr. Reddys Laboratories reached 82.9 percent and Alliance Oncology reached 60.5 percent. This endpoint-only gap removes the sentence naming the main outcome even when the broader study narrative remains visible for readers. These counts describe missing registry text fields and do not by themselves establish legal non-compliance, concealment, or absent materials elsewhere.

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 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-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.