Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Detailed-Description Gap

Which condition families most often leave older CT.gov study pages without detailed descriptions, removing the broad narrative paragraph 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 detailed-description gap as a missing detailed description field, then ranked large condition families by stock and rate. The broad OTHER bucket led the condition-family detailed-description-gap stock table at 18,641 studies, followed by Oncology at 12,321, Cardiovascular at 8,808, and Healthy volunteers at 7,082. Healthy volunteers had the highest large-family detailed-description-gap rate at 50.2 percent, ahead of Immunology and dermatology at 41.7 percent and Renal and urology at 38.3 percent. Condition-family detailed-description gaps show where the broad study narrative disappears most often in major therapeutic areas, not only fringe portfolios. Condition families are keyword-derived registry groupings, not formal disease ontologies or mutually exclusive diagnoses across all studies. They simplify diagnoses into public buckets.

Outside Notes

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