Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Sponsor Text Asymmetry

Which named sponsors show the biggest imbalance between missing detailed descriptions and missing primary-outcome-only text in older CT.gov records? 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 compared description-only gaps against primary-only gaps and defined net text asymmetry as description-only minus primary-only counts. Eli Lilly and Company led the sponsor text-asymmetry table at 1,021 net description-only gaps, followed by GlaxoSmithKline at 1,006, Pfizer at 948, and Boehringer Ingelheim at 841. Johnson and Johnson Vision Care had the highest large-sponsor asymmetry rate at 93.4 percentage points, while Industry reached 18,009 net description-only gaps and NIH flipped negative at minus 1,189. The asymmetry lens shows where the broad study narrative disappears much more often than the endpoint sentence, producing text-thin registry pages. Positive asymmetry does not by itself prove concealment; it shows which field disappears more often inside public registry records overall.

Outside Notes

Type: methods
Primary estimand: Net description-vs-endpoint asymmetry among older studies, defined as description-only gaps minus primary-only gaps
App: CT.gov Sponsor Text Asymmetry dashboard
Data: 249,507 eligible older closed interventional studies with description-only, primary-only, and net text-asymmetry summaries
Code: https://github.com/mahmood726-cyber/ctgov-sponsor-text-asymmetry
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.