Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Primary-Outcome Gap

Which condition families most often leave older CT.gov study pages without primary outcome descriptions, obscuring the main endpoint for readers? 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 a primary-outcome gap as a missing primary outcome description, then ranked large condition families by stock and rate. Oncology led the condition-family primary-outcome-gap stock table at 11,207 studies, followed by the broad OTHER bucket at 10,942, Cardiovascular at 7,006, and Metabolic at 5,006. Healthy volunteers had the sharpest large-family primary-outcome-gap rate at 35.0 percent, ahead of Metabolic at 28.9 percent and Renal and urology at 28.8 percent. Condition-family primary-outcome gaps show where registry pages omit the endpoint-defining sentence in major therapeutic areas, not only smaller fringe portfolios. Condition families are keyword-derived registry groupings rather than formal disease ontologies or mutually exclusive diagnoses across all studies. They simplify diverse diagnoses into usable public buckets.

Outside Notes

Type: methods
Primary estimand: Primary-outcome-gap stock among older studies missing the primary outcome description field
App: CT.gov Condition Primary-Outcome Gap dashboard
Data: 249,507 eligible older closed interventional studies with condition-family primary-outcome-gap stock and rate summaries
Code: https://github.com/mahmood726-cyber/ctgov-condition-primary-outcome-gap
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.