CONSORT-TM: Text classification models for assessing the completeness of randomized controlled trial publications.

Objective: To develop text classification models for determining whether the checklist items in the CONSORT reporting guidelines are reported in randomized controlled trial publications. Materials and Methods: Using a corpus annotated at the sentence level with 37 fine-grained CONSORT items, we trained several sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. To address the problem of small training dataset, we used several data augmentation methods (EDA, UMLS-EDA, text generation and rephrasing with GPT-4) and assessed their impact on the fine-tuned PubMedBERT model. We also fine-tuned PubMedBERT models limited to checklist items associated with specific sections (e.g., Methods) to evaluate whether such models could improve performance compared to the single full model. We performed 5-fold cross-validation and report precision, recall, F1 score, and area under curve (AUC). Results: Fine-tuned PubMedBERT model that takes as input the sentence and the surrounding sentence representations and uses section headers yielded the best overall performance (0.71 micro-F1, 0.64 macro-F1). Data augmentation had limited positive effect, UMLS-EDA yielding slightly better results than data augmentation using GPT-4. BioGPT fine-tuning and GPT-4 in-context learning exhibited suboptimal results. Methods-specific model yielded higher performance for methodology items, other section-specific models did not have significant impact. Conclusion: Most CONSORT checklist items can be recognized reasonably well with the fine-tuned PubMedBERT model but there is room for improvement. Improved models can underpin the journal editorial workflows and CONSORT adherence checks and can help authors in improving the reporting quality and completeness of their manuscripts.

Establishing Institutional Scores With the Rigor and Transparency Index: Large-scale Analysis of Scientific Reporting Quality.

BACKGROUND: Improving rigor and transparency measures should lead to improvements in reproducibility across the scientific literature; however, the assessment of measures of transparency tends to be very difficult if performed manually. OBJECTIVE: This study addresses the enhancement of the Rigor and Transparency Index (RTI, version 2.0), which attempts to automatically assess the rigor and transparency of journals, institutions, and countries using manuscripts scored on criteria found in reproducibility guidelines (eg, Materials Design, Analysis, and Reporting checklist criteria). METHODS: The RTI tracks 27 entity types using natural language processing techniques such as Bidirectional Long Short-term Memory Conditional Random Field-based models and regular expressions; this allowed us to assess over 2 million papers accessed through PubMed Central. RESULTS: Between 1997 and 2020 (where data were readily available in our data set), rigor and transparency measures showed general improvement (RTI 2.29 to 4.13), suggesting that authors are taking the need for improved reporting seriously. The top-scoring journals in 2020 were the Journal of Neurochemistry (6.23), British Journal of Pharmacology (6.07), and Nature Neuroscience (5.93). We extracted the institution and country of origin from the author affiliations to expand our analysis beyond journals. Among institutions publishing >1000 papers in 2020 (in the PubMed Central open access set), Capital Medical University (4.75), Yonsei University (4.58), and University of Copenhagen (4.53) were the top performers in terms of RTI. In country-level performance, we found that Ethiopia and Norway consistently topped the RTI charts of countries with 100 or more papers per year. In addition, we tested our assumption that the RTI may serve as a reliable proxy for scientific replicability (ie, a high RTI represents papers containing sufficient information for replication efforts). Using work by the Reproducibility Project: Cancer Biology, we determined that replication papers (RTI 7.61, SD 0.78) scored significantly higher (P<.001) than the original papers (RTI 3.39, SD 1.12), which according to the project required additional information from authors to begin replication efforts. CONCLUSIONS: These results align with our view that RTI may serve as a reliable proxy for scientific replicability. Unfortunately, RTI measures for journals, institutions, and countries fall short of the replicated paper average. If we consider the RTI of these replication studies as a target for future manuscripts, more work will be needed to ensure that the average manuscript contains sufficient information for replication attempts.

The Rigor and Transparency Index Quality Metric for Assessing Biological and Medical Science Methods.

The reproducibility crisis is a multifaceted problem involving ingrained practices within the scientific community. Fortunately, some causes are addressed by the author's adherence to rigor and reproducibility criteria, implemented via checklists at various journals. We developed an automated tool (SciScore) that evaluates research articles based on their adherence to key rigor criteria, including NIH criteria and RRIDs, at an unprecedented scale. We show that despite steady improvements, less than half of the scoring criteria, such as blinding or power analysis, are routinely addressed by authors; digging deeper, we examined the influence of specific checklists on average scores. The average score for a journal in a given year was named the Rigor and Transparency Index (RTI), a new journal quality metric. We compared the RTI with the Journal Impact Factor and found there was no correlation. The RTI can potentially serve as a proxy for methodological quality.