Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Description Black-Box

Which condition families carry the most older CT.gov studies that are overdue, unlinked, and missing both detailed description and primary outcome description? 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 a description black-box study as one with a two-year results gap, no linked publication, no detailed description, and no primary outcome description, then ranked large condition families. The broad OTHER bucket led the stock table at 3,366 studies, followed by Oncology at 2,619, Healthy volunteers at 2,516, and Cardiovascular at 1,798. Healthy volunteers had the highest large-family description-black-box rate at 17.8 percent, far above renal and urology at 8.5 percent and metabolic at 8.3 percent. The condition-family black-box view mixes diffuse registry stock with a very sharp healthy-volunteer blackout pattern that is more severe than ordinary no-results counts alone. Condition families are keyword-derived registry groupings, not formal disease ontologies or diagnoses.

Outside Notes

Type: methods
Primary estimand: Description black-box stock among older studies with no results, no linked publication, no detailed description, and no primary outcome description
App: CT.gov Condition Description Black-Box dashboard
Data: 249,507 eligible older closed interventional studies with description-black-box stock and rate summaries
Code: https://github.com/mahmood726-cyber/ctgov-condition-description-black-box
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.