Adding value to core outcome set development using multimethod systematic reviews
Bibliographic record
Abstract
Trials evaluating the same interventions rarely measure or report identical outcomes. This limits the possibility of aggregating effect sizes across studies to generate high-quality evidence through systematic reviews and meta-analyses. To address this problem, core outcome sets (COS) establish agreed sets of outcomes to be used in all future trials. When developing COS, potential outcome domains are identified by systematically reviewing the outcomes of trials, and increasingly, through primary qualitative research exploring the experiences of key stakeholders, with relevant outcome domains subsequently determined through transdisciplinary consensus development. However, the primary qualitative component can be time consuming with unclear impact. We aimed to examine the potential added value of a qualitative systematic review alongside a quantitative systematic review of trial outcomes to inform COS development in neonatal care using case analysis methods. We compared the methods and findings of a scoping review of neonatal trial outcomes and a scoping review of qualitative research on parents', patients', and professional caregivers' perspectives of neonatal care. Together, these identified a wider range and greater depth of health and social outcome domains, some unique to each review, which were incorporated into the subsequent Delphi process and informed the final set of core outcome domains. Qualitative scoping reviews of participant perspectives research, used in conjunction with quantitative scoping reviews of trials, could identify more outcome domains for consideration and could provide greater depth of understanding to inform stakeholder group discussion in COS development. This is an innovation in the application of research synthesis methods.
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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 | 0.777 | 0.894 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.025 | 0.020 |
| Bibliometrics | 0.076 | 0.042 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.032 | 0.029 |
| Open science | 0.010 | 0.037 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".