How can clinicians choose between conflicting and discordant systematic reviews? A replication study of the Jadad algorithm
Bibliographic record
Abstract
Abstract Introduction: The exponential growth of published SRs (SRs) presents challenges for clinicians seeking to answer clinical questions. In 1997, an algorithm was created by Jadad et al. to choose the best SR across multiple but similar SRs with conflicting results. Our study aims to replicate assessments done by authors using the Jadad algorithm to determine: (i) if we chose the same SR as the authors; and (ii) if we would reach the same results.Methods and Analysis: We searched MEDLINE, Epistemonikos, and Cochrane Database of SRs. We included any study using the Jadad algorithm. We used consensus building strategies to operationalise the algorithm and to ensure a consistent approach to interpretation.Results: We identified 21 studies that used the Jadad algorithm to choose one or more SRs. In 62% (13/21) of cases, we were unable to replicate the Jadad assessment and ultimately chose a different SR than the authors. Overall, 18 out of the 21 (86%) independent Jadad assessments agreed in direction of the findings despite 13 having chosen a different SR.Conclusions: Our results suggest that the Jadad algorithm is not reproducible between users as there are no prescriptive instructions about how to operationalise the algorithm. In the absence of a validated algorithm, we recommend that healthcare providers, policy makers, patients and researchers address conflicts between review findings by choosing the SR(s) with meta-analysis of RCTs that most closely resemble their clinical, public health, or policy question, are the most recent, comprehensive (i.e. in terms of number of included RCTs), and at the lowest risk of bias.
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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 | 0.834 | 0.955 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".