Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Excess Watchlist

Which condition families remain worst once excess hiddenness is measured inside broad therapeutic portfolios rather than single sponsor tables? 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 ranked condition families by adjusted no-results excess, adjusted ghost excess, black-box stock, and strict-core carryover using the same study-mix adjustment as wave eight. Oncology carried the largest adjusted excess no-results stock at 543 studies, followed by cardiovascular at 373 and metabolic at 251. Healthy volunteers were different: near expected on no-results, yet 1,032 studies above expectation on ghost protocols and a 33.9 percent black-box rate. Condition families therefore split into stock-heavy disease backlogs and a separate healthy-volunteer silence pattern that is much more ghosted than merely overdue inside the same older-study registry universe overall. Condition families are keyword-derived registry groupings, so they approximate therapeutic portfolios rather than adjudicated disease ontologies or mutually exclusive clinical domains.

Outside Notes

Type: methods
Primary estimand: Adjusted excess no-results and ghost stock across CT.gov condition families
App: CT.gov Condition Excess Watchlist dashboard
Data: 249,507 eligible older closed interventional studies with keyword-derived condition-family labels
Code: https://github.com/mahmood726-cyber/ctgov-condition-excess-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.