Quality assessment of observational studies in a drug-safety systematic review, comparison of two tools: the Newcastle–Ottawa Scale and the RTI item bank
Bibliographic record
Abstract
BACKGROUND: The study objective was to compare the Newcastle-Ottawa Scale (NOS) and the RTI item bank (RTI-IB) and estimate interrater agreement using the RTI-IB within a systematic review on the cardiovascular safety of glucose-lowering drugs. METHODS: We tailored both tools and added four questions to the RTI-IB. Two reviewers assessed the quality of the 44 included studies with both tools, (independently for the RTI-IB) and agreed on which responses conveyed low, unclear, or high risk of bias. For each question in the RTI-IB (n=31), the observed interrater agreement was calculated as the percentage of studies given the same bias assessment by both reviewers; chance-adjusted interrater agreement was estimated with the first-order agreement coefficient (AC1) statistic. RESULTS: The NOS required less tailoring and was easier to use than the RTI-IB, but the RTI-IB produced a more thorough assessment. The RTI-IB includes most of the domains measured in the NOS. Median observed interrater agreement for the RTI-IB was 75% (25th percentile [p25] =61%; p75 =89%); median AC1 statistic was 0.64 (p25 =0.51; p75 =0.86). CONCLUSION: The RTI-IB facilitates a more complete quality assessment than the NOS but is more burdensome. The observed agreement and AC1 statistic in this study were higher than those reported by the RTI-IB's developers.
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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 | 0.557 | 0.764 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.035 | 0.028 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".