Helping Trainees Understand the Strategies to Minimize Errors and Biases in Systematic Review Approaches
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
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.
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 | 0.035 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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