Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Overdue Debt

Which condition families hold the deepest overdue debt once unresolved years beyond the two-year mark are added up rather than reduced to missing-results rate? 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 summed overdue years beyond the two-year mark across condition families and compared debt stock with missing-results counts and mean unresolved age. The broad OTHER bucket carried the largest condition-family overdue debt at 289,823 unresolved years, while Oncology was the largest named family at 255,229 and Cardiovascular followed at 154,672. Metabolic and healthy-volunteer portfolios also carried very large overdue debt, while oncology had the heaviest named-family mean unresolved age at 9.0 years. Condition debt mixes broad diffuse registry stock with large named disease portfolios that stay unresolved for years after the reporting window closes. Condition families are keyword-derived registry groupings, so the debt tables describe therapeutic portfolios rather than disease ontologies.

Outside Notes

Type: methods
Primary estimand: Total unresolved years beyond the two-year results mark across CT.gov condition families
App: CT.gov Condition Overdue Debt dashboard
Data: 249,507 eligible older closed interventional studies with condition-family overdue debt, missing-results, and mean unresolved age fields
Code: https://github.com/mahmood726-cyber/ctgov-condition-overdue-debt
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.