Risk of Bias Evaluation of Cross‐Sectional Studies: Adaptation of the Newcastle‐Ottawa Scale
Bibliographic record
Abstract
Cross-sectional studies are widely utilized in medical research to estimate prevalence and examine associations. As such, they can serve as a significant source of data for systematic reviews. However, specific considerations are necessary when evaluating the risk of bias (RoB) of cross-sectional studies, as several potential biases can undermine the validity, reliability, and robustness of their findings. This article introduces a novel, context-specific tool designed to assess the RoB of cross-sectional studies for use in systematic reviews. The proposed tool represents an adaptation of the Newcastle-Ottawa Scale (NOS), originally developed for cohort and case-control studies. Similar to the original NOS, the new tool (named "NOS-xs") features a nine-star rating system to assess six specific items across three main domains: (i) study sample selection, (ii) assessment of exposure(s) and outcome(s), and (iii) confounding factors. Based on the number of awarded stars, studies are categorized as having high (0-3 stars), moderate (4-6 stars), or low (7-9 stars) RoB. The NOS-xs tool maintains consistency with the original NOS tool, facilitating its integration into systematic reviews that also include cohort and/or case-control studies. While the NOS-xs is suited to analytical cross-sectional studies (i.e., association studies), a simplified version ("NOS-xs2") is also introduced for descriptive cross-sectional studies (i.e., prevalence studies). The NOS-xs2 features a four-star rating system to assess three of the six specific items included in the NOS-xs. To streamline their application, spreadsheets for both NOS-xs and NOS-x2 are provided.
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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 | 0.387 | 0.654 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.016 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".