Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Narrative Gap Repeaters

Which named sponsors accumulate the most CT.gov narrative-gap pages where detailed descriptions and primary outcome descriptions are both missing from older study records? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot. We ranked named sponsors with at least 100 older studies by narrative-gap stock and rate, then compared description-black-box counts, condition extremes, and sponsor-class rates. Boehringer Ingelheim carried the largest narrative-gap stock at 706 studies, followed by Hoffmann-La Roche at 555 and GlaxoSmithKline at 526. Mylan Pharmaceuticals Inc had the sharpest large-sponsor narrative-gap rate at 93.7 percent, healthy-volunteer studies reached 22.0 percent, and industry reached 19.5 percent as a sponsor class. The narrative-gap view shows a different kind of registry opacity because many pages retain dates and status fields yet omit the text needed to understand what was actually studied for readers. Narrative gaps here are registry-page omissions, not proof that protocols or outcome descriptions were never written elsewhere.

Outside Notes

Type: methods
Primary estimand: Narrative-gap stock and rate among named lead sponsors with at least 100 older studies
App: CT.gov Narrative Gap Repeaters dashboard
Data: 249,507 eligible older closed interventional studies with narrative-gap fields derived from missing detailed descriptions and missing primary-outcome descriptions
Code: https://github.com/mahmood726-cyber/ctgov-narrative-gap-repeaters
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.