BACKGROUND AND SIGNIFICANCE: Acute otitis media (AOM) is a leading cause of pediatric antibiotic overuse. Safety Net Antibiotic Prescriptions (SNAPs) are recommended for antibiotic stewardship but are difficult to identify due to lack of structured documentation. OBJECTIVE: This study validates the accuracy of Versa, a GPT-4o based HIPAA-compliant large language model (LLM), to classify AOM treatment plans from physician notes. METHODS: A retrospective cross-sectional study analyzed pediatric AOM encounters. Multiple prompting strategies were used to classify treatment plans and validated against a representative sample of manual reviews by 2 pediatricians. A locally fine-tuned model, Clinical-Longformer was also trained and tested against Versa and human review. RESULTS: In total, 5707 encounters were included; 374 reviewed manually. Zero-shot accuracy was 97.8%; few-shot accuracy was 85%. Clinical-Longformer achieved 93.3% accuracy. CONCLUSION: Versa effectively identifies AOM treatment plans, providing a cost-efficient quality improvement tracking tool for prescription practice patterns in pediatric antibiotic stewardship efforts.

We propose a dynamic verification approach for large-scale message passing programs to locate correctness bugs caused by unforeseen nondeterministic interactions. This approach hinges on an efficient protocol to track the causality between nondeterministic message receive operations and potentially matching send operations. We show that causality tracking protocols that rely solely on logical clocks fail to capture all nuances of MPI program behavior, including the variety of ways in which nonblocking calls can complete. Our approach is hinged on formally defining the matches-before relation underlying the MPI standard, and devising lazy update logical clock based algorithms that can correctly discover all potential outcomes of nondeterministic receives in practice. can achieve the same coverage as a vector clock based algorithm while maintaining good scalability. LLCP allows us to analyze realistic MPI programs involving a thousand MPI processes, incurring only modest overheads in terms of communication bandwidth, latency, and memory consumption.

Standard testing methods of MPI programs do not guarantee coverage of all non-deterministic interactions (e.g., wildcard-receives). Programs tested by these methods can have untested paths (bugs) that may become manifest unexpectedly. Previous formal dynamic verifiers cover the space of non-determinism but do not scale, even for small applications. We present DAMPI, the first dynamic analyzer for MPI programs that guarantees scalable coverage of the space of non-determinism through a decentralized algorithm based on Lamport-clocks. DAMPI computes alternative non-deterministic matches and enforces them in subsequent program replays. To avoid interleaving explosion, DAMPI employs heuristics to focus coverage to regions of interest. We show that DAMPI can detect deadlocks and resource-leaks in real applications. Our results on a wide range of applications using over a thousand processes, which is an order of magnitude larger than any previously reported results for MPI dynamic verification tools, demonstrate that DAMPI provides scalable, user-configurable testing coverage.

This paper considers the problem of formal verification of MPI programs operating under a fixed test harness for safety properties without building verification models. In our approach, we directly model-check the MPI/C source code, executing its interleavings with the help of a verification scheduler. Unfortunately, the total feasible number of interleavings is exponential, and impractical to examine even for our modest goals. Our earlier publications formalized and implemented a partial order reduction approach that avoided exploring equivalent interleavings, and presented a verification tool called ISP. This paper presents algorithmic and engineering innovations to ISP, including the use of OpenMP parallelization, that now enables it to handle practical MPI programs, including: (i) ParMETIS- a widely used hypergraph partitioner, and (ii) MADRE- a Memory Aware Data Re-distribution Engine, both developed outside our group. Over these benchmarks, ISP has automatically verified up to 14K lines of MPI/C code, producing error traces of deadlocks and assertion violations within seconds.