AppRaise: Software for Quantifying Evidence Uncertainty in Systematic Reviews Using a Posterior Mixture Model
Bibliographic record
Abstract
RATIONALE: Systematic reviews are essential for evidence-based healthcare decision-making. While it is relatively straightforward to quantitatively assess random errors in systematic reviews, as these are typically reported in primary studies, the assessment of biases often remains narrative. Primary studies seldom provide quantitative estimates of biases and their uncertainties, resulting in systematic reviews rarely including such measurements. Additionally, evidence appraisers often face time constraints and technical challenges that prevent them from conducting quantitative bias assessments themselves. Given that multiple biases and random errors collectively skew the point estimate from the truth, it is important to incorporate comprehensive quantitative methods of uncertainty in systematic reviews. These methods should integrate random errors and biases into a unified measure of uncertainty and be easily accessible to evidence appraisers, preferably through user-friendly software. AIMS AND OBJECTIVES: To address this need, we propose a posterior mixture model and introduce AppRaise, a free, web-based interactive software designed to implement this approach. METHODS: We showcase its application through a health technology assessment (HTA) report on the effectiveness of continuous glucose monitoring in reducing A1c levels among individuals with type 1 diabetes. RESULTS: Applying the AppRaise software to the HTA report revealed a high level of certainty (86% probability) that continuous glucose monitoring would, on average, result in a reduction in A1c levels compared with self-monitoring of blood glucose among Ontarians with type 1 diabetes. These findings were similar to other quantitative bias-adjusted approaches in systematic reviews. CONCLUSION: AppRaise can be utilized as a standalone tool or as a complement to validate the quality of evidence assessed using qualitative-based scoring methods. This approach is also useful for assessing the sensitivity of parameter estimates to potential biases introduced by primary studies.
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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 | 0.140 | 0.401 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".