Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Narrative Gap

Which condition families most often leave older CT.gov study pages without both detailed descriptions and primary outcome descriptions? 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 narrative-gap study as one missing both detailed description and primary outcome description, then ranked large condition families by stock and rate. The broad OTHER bucket led the narrative-gap stock table at 5,124 studies, followed by Oncology at 4,105, Cardiovascular at 3,240, and Healthy volunteers at 3,100. Healthy volunteers had the sharpest large-family narrative-gap rate at 22.0 percent, ahead of Metabolic at 14.8 percent and Renal and urology at 14.6 percent. Condition-family narrative gaps show where registry pages stay text-thin even before readers ask whether results or publications were posted later. Condition families are keyword-derived registry groupings, not formal disease ontologies or mutually exclusive diagnoses for readers. They simplify diverse diagnoses into usable public buckets.

Outside Notes

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