Identifying and addressing conflicting results across multiple discordant systematic reviews on the same question: Protocol for a replication study of the Jadad algorithm
Bibliographic record
Abstract
Abstract Introduction: An increasing growth of systematic reviews (SRs) presents notable challenges for decision-makers seeking to answer clinical questions. In 1997, an algorithm was created by Jadad to assess discordance in results across SRs on the same question. Our study aims to (a) replicate assessments done in a sample of studies using the Jadad algorithm to determine if the same SR would have been chosen, (b) evaluate the Jadad algorithm in terms of utility, efficiency, and comprehensiveness, and (c) describe how authors address discordance in results across multiple SRs. Methods and Analysis: We will use a database of 1218 overviews (2000-2020) created from a bibliometric study as the basis of our search for studies assessing discordance (called Discordant Reviews). This bibliometric study searched MEDLINE (Ovid), Epistemonikos, and Cochrane Database of Systematic Reviews for overviews. We will include any study using Jadad (1997) or another method to assess discordance. The first 30 studies screened at the full-text stage by two independent reviewers will be included. We will replicate the authors’ Jadad assessments. We will compare our outcomes qualitatively and evaluate the differences between our Jadad assessment of discordance and the authors’ assessment. Ethics and Dissemination: No ethics approval was required as no human subjects were involved. In addition to publishing in an open-access journal, we will disseminate evidence summaries through formal and informal conferences, academic websites, and across social media platforms. This is the first study to comprehensively evaluate and replicate Jadad algorithm assessments of discordance across multiple SRs.
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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 | 0.273 | 0.419 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".