Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Outcome-Density Gap

Does richer outcome specification correspond to a more visible CT.gov record once older closed interventional studies are grouped by outcome density? We analysed 249,507 eligible older closed interventional studies from the March 29, 2026 full-registry snapshot and bucketed primary outcomes, secondary outcomes, and primary-outcome description fields. The project compares two-year no-results rates, ghost-protocol rates, full visibility, and description contrasts across sparse and dense outcome structures. Studies with zero recorded primary outcomes show a 100.0 percent no-results rate and a 65.1 percent ghost-protocol rate. Studies with ten or more secondary outcomes fall to 56.7 percent no results, while studies missing primary-outcome descriptions still reach 94.4 percent no results. Outcome density therefore looks like a proxy for public record seriousness: sparser protocols are far more likely to remain hidden. The gradient survives across counts, text fields, and both primary and secondary outcome layers. Outcome counts capture declared registry structure rather than scientific importance, statistical hierarchy, or endpoint quality.

Outside Notes

Type: methods
Primary estimand: 2-year no-results rate across outcome-density buckets among eligible older CT.gov studies
App: CT.gov Outcome-Density Gap dashboard
Data: 249,507 eligible older closed interventional studies grouped by outcome counts and outcome-description fields
Code: https://github.com/mahmood726-cyber/ctgov-outcome-density-gap
Version: 1.0.0
Validation: FULL REGISTRY RUN

References

1. ClinicalTrials.gov API v2. National Library of Medicine. Accessed March 29, 2026.
2. Zarin DA, Tse T, Williams RJ, Carr S. Trial reporting in ClinicalTrials.gov. N Engl J Med. 2016;375(20):1998-2004.
3. 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.

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.