Mahmood Ahmad
Tahir Heart Institute
author@example.com

Protocol: MetaSprint DTA: Automated Open-Access Discovery for Diagnostic Test Accuracy Meta-Analysis

This protocol describes the evidence synthesis for MetaSprint DTA: Automated Open-Access Discovery for Diagnostic Test, targeting reproducible estimation of Pooled sensitivity and specificity in a versioned workflow. Eligible studies include randomised controlled trials reporting the primary endpoint in the target population, with no restrictions on year, language, or sample size. Searches will cover PubMed, Embase, and the Cochrane Central Register using structured strategies, reference-list screening, and duplicate review before extraction. The primary analysis will estimate Pooled sensitivity and specificity using restricted maximum likelihood random-effects meta-analysis, reporting 95 percent confidence intervals, prediction intervals, and prespecified model checks. Heterogeneity will be summarised using I-squared and tau-squared, with sensitivity analyses across variance estimators, exclusion scenarios, and leave-one-out patterns. Analysis code will be versioned and archived at https://github.com/mahmood726-cyber/metasprint-dta, and reporting will follow PRISMA 2020 guidance to support independent verification and reuse. Anticipated limitations include publication bias, clinical heterogeneity, sparse data in some settings, and the constraints of aggregate-level evidence synthesis.

Outside Notes

Type: protocol
Primary estimand: Pooled sensitivity and specificity
App: MetaSprint DTA v1.0
Code: https://github.com/mahmood726-cyber/metasprint-dta
Date: 2026-03-26
Validation: DRAFT

References

1. Reitsma JB, Glas AS, Rutjes AW, et al. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol. 2005;58(10):982-990.
2. Macaskill P, Gatsonis C, Deeks JJ, Harbord RM, Takwoingi Y. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Cochrane; 2023.
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 (i.e., a structured, pipeline-based synthesis output). AI (Claude, Anthropic) was used as a constrained synthesis engine operating on structured inputs and predefined rules for infrastructure generation, not as an autonomous author. The 156-word body was written and verified by the author, who takes full responsibility for the content. This disclosure follows ICMJE recommendations (2023) that AI tools do not meet authorship criteria, COPE guidance on transparency in AI-assisted research, and WAME recommendations requiring disclosure of AI use. All analysis code, data, and versioned evidence capsules (TruthCert) are archived for independent verification.