Helping Trainees Understand the Strategies to Minimize Errors and Biases in Systematic Review Approaches
Bibliographic record
Abstract
Over the past few decades, there has been major development in methods for evidence synthesis, which can lead to confusion as to which approaches to use and why. Several strategies can be used in systematic review approaches to reduce potential biases and errors. These strategies can be considered on a spectrum ranging from least to most likely to minimize biases and errors in the review process. Building on the existing literature of synthesis methods and biases, a five-level spectrum of systematicity in reviews is proposed in this paper. For each of the main steps of the review process (i.e. search, selection, data extraction, appraisal, and synthesis), potential biases are presented. Then, strategies are suggested and ordered based on their influence on potential biases and errors in the review process. The levels of systematicity suggested can help to distinguish the reviews based on their rigour. This paper can contribute to improving understanding of the variety of strategies that can be used at the different steps of a review process. This can be particularly useful for students and novice researchers seeking to understand the potential sources of bias and to choose suitable strategies for their review.
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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 | 0.492 | 0.757 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".