Prioritising Cochrane reviews to be updated with health equity focus
Bibliographic record
Abstract
BACKGROUND: The prioritisation of updating published systematic reviews of interventions is vital to prevent research waste and ensure relevance to stakeholders. The consideration of health equity in reviews is also important to ensure interventions will not exacerbate the existing inequities of the disadvantaged if universally implemented. This study aimed to pilot a priority setting exercise based on systematic reviews of interventions published in the Cochrane Library, to identify and prioritise reviews to be updated with a focus on health equity. METHODS: We conducted a priority setting exercise with a group of 13 international stakeholders. We identified Cochrane reviews of interventions that showed a reduction in mortality, had at least one Summary of Findings table and that focused on one of 42 conditions with a high global burden of disease from the 2019 WHO Global Burden of Disease report. This included 21 conditions used as indicators of success of the United Nations Universal Health Coverage in attaining the Sustainable Development Goals. Stakeholders prioritised reviews that were relevant to disadvantaged populations, or to characteristics of potential disadvantage within the general population. RESULTS: After searching for Cochrane reviews of interventions within 42 conditions, we identified 359 reviews that assessed mortality and included at least one Summary of Findings table. These pertained to 29 of the 42 conditions; 13 priority conditions had no reviews with the outcome mortality. Reducing the list to only reviews showing a clinically important reduction in mortality left 33 reviews. Stakeholders ranked these reviews in order of priority to be updated with a focus on health equity. CONCLUSIONS: This project developed and implemented a methodology to set priorities for updating systematic reviews spanning multiple health topics with a health equity focus. It prioritised reviews that reduce overall mortality, are relevant to disadvantaged populations, and focus on conditions with a high global burden of disease. This approach to the prioritisation of systematic reviews of interventions that reduce mortality provides a template that can be extended to reducing morbidity, and the combination of mortality and morbidity as represented in Disability-Adjusted Life Years and Quality-Adjusted Life Years.
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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 | 0.505 | 0.790 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.101 | 0.053 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.012 | 0.021 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".