Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Ghost Watchlist

Which condition families remain most ghosted once the series stops centering missing-results stock and instead ranks excess ghost protocols? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot using one condition-family label per study. Using the wave-nine condition watchlist, we ranked condition families by excess ghost stock, raw ghost counts, black-box stock, and black-box rates. Healthy volunteers carried the largest condition-family ghost excess at 1,032 studies, far ahead of the broader OTHER bucket at 552 and musculoskeletal and pain at 333. Gastrointestinal and hepatic portfolios also remained above expectation, while several major disease families such as oncology and cardiovascular were below expectation on this stricter ghost target. The ghost table therefore identifies a different silence pattern than the no-results table, centered on healthy-volunteer and diffuse non-disease portfolios with unusually thin and fragmented public traces. Condition families are keyword-derived registry groupings, so they approximate therapeutic portfolios rather than adjudicated disease ontologies.

Outside Notes

Type: methods
Primary estimand: Excess ghost-protocol stock across condition families
App: CT.gov Condition Ghost Watchlist dashboard
Data: 249,507 eligible older closed interventional studies with condition-family ghost watchlists derived from the wave-nine tables
Code: https://github.com/mahmood726-cyber/ctgov-condition-ghost-watchlist
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.