Mahmood Ahmad
Tahir Heart Institute
author@example.com

CT.gov Sponsor Backlog Concentration

Is the ClinicalTrials.gov missing-results backlog spread evenly across sponsors, or does a relatively small slice hold most of the unresolved stock? We analysed sponsor-level counts for 249,507 eligible older closed interventional studies and ranked 25,584 lead sponsors by two-year missing-results volume. The concentration analysis tracked cumulative shares of unresolved no-results studies, sponsor-level ghost-protocol counts, and inequality metrics alongside named outlier sponsors. The top 1 percent of lead sponsors accounted for 39.6 percent of the missing-results backlog, and the top 10 percent accounted for 77.4 percent. The sponsor-level Gini coefficient reached 0.818, while large industry firms, major academic centers, and public institutions all appeared among the highest-volume sponsors. The unresolved stock is therefore broad but highly uneven, with a thin sponsor slice carrying a disproportionate share of what remains unseen. These concentration statistics describe registry-visible stock distribution, not legal liability, and they depend on the lead-sponsor field recorded in CT.gov across this sponsor field and snapshot frame.

Outside Notes

Type: methods
Primary estimand: Share of the 2-year missing-results backlog held by top sponsor slices
App: CT.gov Sponsor Backlog Concentration dashboard
Data: 25,584 lead sponsors contributing to the eligible older missing-results backlog in the March 29, 2026 snapshot
Code: https://github.com/mahmood726-cyber/ctgov-sponsor-backlog-concentration
Version: 1.0.0
Validation: FULL REGISTRY RUN

References

1. ClinicalTrials.gov API v2. National Library of Medicine. Accessed March 29, 2026.
2. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement. BMJ. 2021;372:n71.
3. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. 2nd ed. Wiley; 2021.

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.