NOS-TLPlot: A Specialized Python Tool for Visualizing Newcastle–Ottawa Scale Risk-of-Bias Assessments in Systematic Reviews and Meta-Analysis.
Bibliographic record
Abstract
Objective: To develop a specialized Python tool for visualizing Newcastle–Ottawa Scale (NOS) risk-of-bias assessments that addresses the limitations of existing generic visualization tools.Methods: The author developed NOS-TLPlot, an open-source Python package and accompanying Streamlit-based web application. The tool automatically converts raw NOS star ratings (0–9) into standardized risk categories (Low, Moderate, High) and generates twelve distinct visualization types, including star distribution plots, traffic-light plots, radar charts, heatmaps, dot profiles, donut charts, and lollipop charts. It offers both an interactive web application and a command-line interface for batch processing and integration into reproducible analytical pipelines. Users can customize output themes (e.g., traffic-light or grayscale), figure sizes, and export formats (PNG, PDF, SVG, EPS).Results: NOS-TLPlot successfully produces publication-quality visualizations for NOS risk-of-bias assessments. The tool's web application provides an intuitive interface for data upload, plot selection, and customization. The command-line interface allows for automated plot generation, facilitating reproducibility. The variety of visualization options enhances the flexibility and comprehensiveness of risk-of-bias reporting.Conclusion: NOS-TLPlot provides a dedicated, user-friendly, and reproducible solution for NOS data visualization, enhancing the transparency, consistency, and efficiency of reporting study quality in systematic reviews and meta-analyses involving non-randomized studies.Keywords: Newcastle–Ottawa Scale, risk of bias, data visualization, Python, systematic review, meta-analysis, open-source software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".