  Failure is common in clinical trials since the successful failures presented
in negative results always indicate the ways that should not be taken. In this
paper, we proposed an automated approach to extracting positive and negative
clinical research results by introducing a PICOE (Population, Intervention,
Comparation, Outcome, and Effect) framework to represent randomized controlled
trials (RCT) reports, where E indicates the effect between a specific I and O.
We developed a pipeline to extract and assign the corresponding statistical
effect to a specific I-O pair from natural language RCT reports. The extraction
models achieved a high degree of accuracy for ICO and E descriptive words
extraction through two rounds of training. By defining a threshold of p-value,
we find in all Covid-19 related intervention-outcomes pairs with statistical
tests, negative results account for nearly 40%. We believe that this
observation is noteworthy since they are extracted from the published
literature, in which there is an inherent risk of reporting bias, preferring to
report positive results rather than negative results. We provided a tool to
systematically understand the current level of clinical evidence by
distinguishing negative results from the positive results.
