Development, Application and Evaluation of Risk of Bias Criteria for Case-Crossover and Time-Series Studies of Air Pollution and Health
Bibliographic record
Abstract
Systematic review and meta-analysis methods are increasingly being applied to environmental health literature. However, these methods have not been routinely applied within the context of formal risk assessments, despite common aims and practices, including systematic identification, analysis and summary assessment of the weight of evidence linking exposures and outcomes. A particular gap in these practices is the availability of standardized criteria for assessing risk of bias in studies of environmental exposures, operationalized in a form that can be efficiently and reliably applied by reviewers / risk assessors to a potentially large number of primary studies. Building on the Navigation Guide systematic review methodology, we developed, applied and evaluated risk of bias criteria applicable to time series and case-crossover studies linking air pollution and cardiovascular and respiratory morbidity, operationalizing them in DistillerSR™ systematic review software in the context of a systematic review of health effects of nitrogen dioxide. Risk of bias domains comprised: selection bias and generalizability, exposure assessment, confounding, outcome assessment, completeness of outcome data, selective outcome reporting, conflict of interest and other sources of bias. Risk of bias criteria were developed through literature review and expert consultation and evaluated with respect to content and face validity, inter-rater agreement and completion time. Our findings address the feasibility and reliability of our risk of bias criteria for time series and case-crossover studies linking air pollution and cardiovascular and respiratory morbidity. These criteria may provide a promising tool in the context of both systematic review and risk assessment. PROSPERO registration number CRD42018084497.
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 | 0.735 | 0.874 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.047 | 0.032 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".