Choosing the right journal for your systematic review
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: The importance of systematic reviews (SRs) as an aid to decision making in health care has led to an increasing interest in the development of this type of study. When selecting a target journal for publication, authors generally seek out higher impact factor journals. This study aimed to determine the percentage of scientific medical journals that publish SRs according to their impact factors (>2.63) and to determine whether those journals require tools that aim to improve SR reporting and meta-analyses. METHODS: In our cross-sectional study showing how to choose the right journal for a SR, we selected and analysed scientific journals available in a digital library with a minimum Institute for Scientific Information impact factor of 2.63. RESULTS: We analysed 622 scientific journals, 435 (69.94%) of which publish SRs. Of those 435 journals, 135 (21.60%) provide instructions for authors that mention SRs. Three hundred journals (48.34%) do not discuss criteria for article acceptance in the instructions for authors section, but do publish SRs. Only 118 (27.00%) scientific journals require items to be reported in accordance with the specific SR reporting forms. CONCLUSIONS: The majority of the journals do not mention the acceptance of SRs in the instructions for authors section. Only a few journals require that SRs meet specific reporting guidelines, making interpretation of their findings across studies challenging. There is no correlation between the impact factor of the journal and its acceptance of SRs for publication.
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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 | 0.350 | 0.589 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.029 | 0.015 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.020 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.022 |
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".