Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Ancient Backlog

Which condition families still hold the largest stock of CT.gov studies unresolved at least ten overdue years beyond the two-year reporting mark? 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 ancient backlog as older studies with no posted results and at least ten overdue years beyond the two-year mark, then ranked large condition families. Oncology led the named-family table at 11,369 studies, while the broad OTHER bucket held 10,899 and cardiovascular followed at 6,545. Metabolic remained high on stock at 4,693, while healthy volunteers reached the highest large-family ancient-backlog rate at 31.5 percent. Ancient backlog separates diffuse registry stock from large disease portfolios and shows that very old silence remains prominent in major therapeutic areas. Condition families are keyword-derived registry groupings, so they approximate therapeutic portfolios rather than fixed clinical ontologies or mutually exclusive diagnoses within the registry as presented here.

Outside Notes

Type: methods
Primary estimand: Ancient-backlog stock among older closed interventional studies unresolved at least ten overdue years beyond the two-year mark
App: CT.gov Condition Ancient Backlog dashboard
Data: 249,507 eligible older closed interventional studies with ancient-backlog stock, rate, and overdue-years summaries
Code: https://github.com/mahmood726-cyber/ctgov-condition-ancient-backlog
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.