Factors associated with successful publication for systematic review protocol registration: an analysis of 397 registered protocols
Bibliographic record
Abstract
BACKGROUND: Meta-analyses are on top of the evidence-based medicine pyramid, yet many of them are not completed after they are begun. Many factors impacting the publication of meta-analysis works have been discussed, and their association with publication likelihood has been investigated. These factors include the type of systematic review, journal metrics, h-index of the corresponding author, country of the corresponding author, funding sources, and duration of publication. In our current review, we aim to investigate these various factors and their impact on the likelihood of publication. A comprehensive review of 397 registered protocols retrieved from five databases was performed to investigate the different factors that might affect the likelihood of publication. These factors include the type of systematic review, journal metrics, h-index of the corresponding author, country of the corresponding author, funding sources, and duration of publication. RESULTS: We found that corresponding authors in developed countries and English-speaking countries had higher likelihoods of publication: 206/320 (p = 0.018) and 158/236 (p = 0.006), respectively. Factors affecting publications are the countries of corresponding author (p = 0.033), whether they are from developed countries (OR: 1.9, 95% CI: 1.2-3.1, p = 0.016), from English-speaking countries (OR: 1.8, 95% CI: 1.2-2.7, p = 0.005), update status of the protocol (OR: 1.6, 95% CI: 1.0-2.6, p = 0.033), and external funding (OR: 1.7, 95% CI: 1.1-2.7, p = 0.025). Multivariable regression retains three variables as significant predictors for the publication of a systematic review: whether it is the corresponding author from developed countries (p = 0.013), update status of the protocol (p = 0.014), and external funding (p = 0.047). CONCLUSION: Being on top of the evidence hierarchy, systematic review and meta-analysis are the keys to informed clinical decision-making. Updating protocol status and external funding are significant influences on their publications. More attentions should be paid to the methodological quality of this type of publication.
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How this classification was reachedexpand
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 | 0.326 | 0.333 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.028 | 0.006 |
| Bibliometrics | 0.002 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads 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".