Checklists vs. checkmate: Reproducibility key to premium surgery success

Traditionally, checkmate is a position in the game of chess in which a player’s king is in check, without a way to remove the threat. The king cannot be captured, so the game ends when the king is checkmated. As a premium surgeon, no one ever wants to be checkmated at any stage of the surgical process, from preoperative to intraoperative to postoperative. Other forms of etymology have suggested checkmate to signify being “ambushed,” a feeling many of us have experienced in our surgical careers. A means to avoiding being a checkmated surgeon is creating “checklists” from the time of the first patient encounter until the final postoperative visit. The process of checklists can bring reproducibility to a surgical process that already yields successful outcomes in a premium surgeon’s practice.

Reproducibility in research results: the challenges of attributing reliability

Studies indicate, however, that more than half of the experiments involving clinical trials of new drugs and treatments are irreproducible. John Ioannidis at Stanford University, US, goes on saying that most of the search results is actually false. Ioannidis is the author of a mathematical model that predicts that the smaller the sample and less stringent are the experimental methodology, definitions, outcomes and statistical analysis, the greater the probability of error. Furthermore, studies that hold financial and other interests or of great impact are also more prone to false results.

Ten Major Errors in Obesity Research Discussed

A paper from investigators at the University of Alabama at Birmingham recently published in Obesity identifies several key statistical errors commonly seen in obesity research with discussions on how to identify and avoid making these mistakes. "Our goal is to provide researchers and reviewers with a tutorial to improve the rigor of the science in future obesity studies,” said Brandon George, Ph.D., statistician in the University of Alabama at Birmingham Office of Energetics. “Investigators who conduct primary research may find the paper useful to read or share with statistical collaborators to obtain a deeper understanding of statistical issues, avoid making the discussed errors, and increase the reproducibility and rigor of the field. Editors, reviewers and consumers will find valuable information allowing them to properly identify these common errors while critically reading the work of others."

Reproducibility in density functional theory calculations of solids

The scrutiny of the scientific community has also turned to research involving computer programs, finding that reproducibility depends more strongly on implementation than commonly thought. These problems are especially relevant for property predictions of crystals and molecules, which hinge on precise computer implementations of the governing equation of quantum physics. We devised a procedure to assess the precision of DFT methods and used this to demonstrate reproducibility among many of the most widely used DFT codes.

Automatic Benchmark Profiling through Advanced Trace Analysis

Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. It computes unified benchmark profiles reflecting benchmarks’ duration, function repartition, stability, CPU efficiency, parallelization and memory usage. It identifies the needed system information for profile computation, collects it from execution traces and produces profiles through efficient and reproducible trace analysis treatments. The paper presents the design, implementation and the evaluation of the approach. The analysis of the kernel trace follows a workflow implemented using the VisTrails tool.

Failure Is Moving Science Forward

As science grapples with what some have called a reproducibility crisis, replication studies, which aim to reproduce the results of previous studies, have been held up as a way to make science more reliable. It seems like common sense: Take a study and do it again — if you get the same result, that’s evidence that the findings are true, and if the result doesn’t turn up again, they’re false. Yet in practice, it’s nowhere near this simple.