Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Condition Text Asymmetry

Which condition families show the biggest imbalance between missing detailed descriptions and missing primary-outcome-only text in older CT.gov records? 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 compared description-only gaps against primary-only gaps and defined net text asymmetry as description-only minus primary-only counts and rates. Other led the condition-family text-asymmetry table at 7,699 net description-only gaps, followed by Musculoskeletal and pain at 2,521, Healthy volunteers at 2,134, and Cardiovascular at 1,802. Immunology and dermatology had the highest condition asymmetry rate at 19.7 percentage points, while Healthy volunteers reached 15.1 points and Neurology 15.0 points. The asymmetry lens shows which therapeutic portfolios lose the broader study narrative much more often than the endpoint sentence, changing how text opacity is distributed for readers. Positive asymmetry does not by itself prove concealment; it shows which field disappears more often inside mature public registry records overall.

Outside Notes

Type: methods
Primary estimand: Condition-family text asymmetry, defined as description-only gaps minus primary-only gaps
App: CT.gov Condition Text Asymmetry dashboard
Data: 249,507 eligible older closed interventional studies with condition-family description-only, primary-only, and net text-balance summaries
Code: https://github.com/mahmood726-cyber/ctgov-condition-text-asymmetry
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.