Association between statistical significance and time to publication among systematic reviews: a study protocol for a meta-epidemiological investigation
Bibliographic record
Abstract
INTRODUCTION: Many studies have indicated the impact of bias in dissemination and publication in medical research. Existence of such bias among clinical trials has been repeatedly pointed out, but it has not been well studied in the field of systematic reviews (SRs). We therefore aim to investigate whether or not time lag bias and publication bias in SRs based on statistical significance in results exist. In addition, we will examine at what stage of paper publication process such bias, if any, creeps in. METHOD AND ANALYSIS: The present study is a meta-epidemiological study. We will include all SRs of interventions registered in the international prospective register of SRs (PROSPERO) before December 2014 if the SR has completed its analysis irrespective of its publication status. All contact authors of eligible SRs will be asked to participate in a survey administered through the Internet. Our primary outcome is time from protocol registration to full publication of SR as a journal article, defined as time from the registration date to the acceptance date among all the relevant SRs. We will examine the impact of statistically significant findings on the primary outcomes through time to event analyses. ETHICS AND DISSEMINATION: Ethics approval will be obtained from the Ethical Committee of the Kyoto University Graduate School of Medicine. This protocol has been registered in the University Hospital Medical Information Network Clinical Trials Registry. We will publish our findings in a peer-reviewed journal and also may present them at conferences. TRIAL REGISTRATION NUMBER: UMIN000028325.
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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 | MetaresearchMeta-epidemiology (broad)Meta-epidemiology (narrow) Domain: Evaluation · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Meta-epidemiology (narrow)Meta-epidemiology (broad)Metaresearch Domain: Evaluation · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | 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 | 0.238 | 0.340 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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.
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".