Pitfalls of Systematic Reviews and Meta-Analyses in Imaging Research
Bibliographic record
Abstract
Systematic reviews of imaging research represent a tool to better understand test accuracy or the efficacy of interventions. Like any type of research, appropriate methods must be applied to optimize quality. The purpose of this review is to outline common pitfalls in performing systematic reviews of imaging research, with a focus on challenges particular to performing reviews of diagnostic accuracy studies. The following challenges are highlighted: posing relevant review questions, conducting comprehensive literature searches, assessing for bias in included studies, testing for heterogeneity and publication bias, pooling results across studies, and forming appropriate conclusions. By guiding authors on how to overcome these, the hope is that published reviews in imaging research will be of higher quality and have a positive impact on clinical practice. In addition, the review aims to educate readers of reviews so they become aware of crucial elements of systematic reviews that could bias review results.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.782 | 0.893 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.016 |
| Bibliometrics | 0.026 | 0.033 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.012 | 0.015 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".