Risk of bias in cross-sectional studies: Protocol for a scoping review of concepts and tools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cross-sectional studies are commonly used to study human health and disease, but are especially susceptible to bias. This scoping review aims to identify and describe available tools to assess the risk of bias (RoB) in cross-sectional studies and to compile the key bias concepts relevant to cross-sectional studies into an item bank. Using the JBI scoping review methodology, the strategy to locate relevant RoB concepts and tools is a combination of database searches, prospective review of PROSPERO registry records; and consultation with knowledge users and content experts. English language records will be included if they describe tools, checklists, or instruments which describe or permit assessment of RoB for cross-sectional studies. Systematic reviews will be included if they consider eligible RoB tools or use RoB tools for RoB of cross-sectional studies. All records will be independently screened, selected, and extracted by one researcher and checked by a second. An analytic framework will be used to structure the extraction of data. Results for the scoping review are pending. Results from this scoping review will be used to inform future selection of RoB tools and to consider whether development of a new RoB tool for cross-sectional studies is needed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.355 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it