ArticleCureus2026
A Transparent, Simple, Audit-Oriented Python-Based Binary Allocation Prototype for Clinical Research.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
1 author.
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Abstract
Background Clinical randomization requires more than approximate 1:1 allocation. It also requires sequence generation that is difficult to subvert, allocation concealment during enrollment, and an audit trail that can withstand retrospective review. Objective The primary objective of this proof-of-concept technical and methodological evaluation was to assess the inspectability and audit-oriented design of a lightweight Python-based two-arm allocation prototype. Secondary objectives were to characterize its short-run demonstration behavior and compare its transparency, traceability, and operational limitations with common clinical randomization workflows. Methods We performed a static review of the supplied Python source file and a retrospective review of the supplied allocation log. Log analysis included descriptive arm counts, exact binomial testing for 1:1 balance, an exploratory runs test, and lag-1 autocorrelation. The implementation was interpreted in light of the clinical-trial randomization and pseudorandom-number-generator literature. Results The current source code implements a lightweight two-arm allocation randomization prototype with features intended to support auditability and tamper-evident traceability. Each allocation advances a xorshift-inspired 64-bit generator, maps the resulting integer to arm 1 or 2, and records the underlying output and assignment arithmetic in a human-readable log. An example test log was run and contained 2,000 allocations, with 502 versus 498 assignments in the first 1,000 events and 1008 versus 992 assignments overall. The current source code also implements session seed capture and a chained SHA-256 digest intended to strengthen sequential traceability and show evidence of tampering. Conclusions The prototype's principal improvement over many ad hoc local workflows is operational transparency rather than proven statistical superiority. Its strongest contribution is a short, inspectable code path and an audit-oriented logging structure. Additional hardening would be required before use in concealment-sensitive or regulated trial settings.
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