Factors Associated With Successful Publication for Systematic Review Protocol Registration: an Analysis of 397 Registered Protocols.
Bibliographic record
Abstract
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. Our models of multivariable logistic regression analysis revealed that two main factors impact the publication outcome: updated protocol status of the published review paper (OR: 1.7, 95% CI: 1.0-2.8, p=0.037) and external funding (OR: 2.1, 95% CI: 1.2-3.8, p=0.01). However, corresponding authors’ location in developed countries (OR: 1.7, 95% CI: 0.8-3.4, p=0.139) and English-speaking countries (OR: 1.5, 95% CI: 0.9-2.6, p=0.1) were insignificant determinants.Conclusion: Meta-analyses continue to be on top of the evidence hierarchy, rendering them the key to informed clinical decision-making. Therefore, more attention should be paid to the methodological quality of this type of 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 | 0.411 | 0.787 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.016 | 0.036 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".