Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Hiddenness Map

Which therapeutic areas look quietest in ClinicalTrials.gov once older closed interventional studies are grouped into comparable condition families? We analysed 249,507 eligible older studies from the March 29, 2026 full-registry snapshot and assigned each record to one dominant keyword-based family using registry condition strings and titles. Primary comparisons focused on ghost-protocol rates, two-year no-results rates, and the share with both results and publication visibility across common families. Oncology formed the largest named family at 42,344 eligible older studies, creating the biggest absolute stock of hidden evidence. Healthy-volunteer studies had the highest ghost-protocol rate among common families at 63.5 percent, while metabolic and gastrointestinal groupings also remained heavily obscured. Infectious-disease studies were relatively more visible, reaching a 20.6 percent fully visible rate despite still carrying substantial non-reporting across mapped families in this atlas. Because the classification is keyword-based and single-label, multi-topic trials can be compressed into one family and some records remain in a broad other bucket.

Outside Notes

Type: methods
Primary estimand: Ghost-protocol rate by keyword-classified condition family among eligible older closed interventional studies
App: CT.gov Condition Hiddenness Map dashboard
Data: Eligible older closed interventional studies classified into dominant condition families from registry condition strings and titles
Code: https://github.com/mahmood726-cyber/ctgov-condition-hiddenness-map
Version: 1.0.0
Validation: FULL REGISTRY RUN

References

1. ClinicalTrials.gov API v2. National Library of Medicine. Accessed March 29, 2026.
2. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement. BMJ. 2021;372:n71.
3. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. 2nd ed. Wiley; 2021.

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.