Should we adopt the case report format to report challenges in complicated evidence synthesis? A proposal and illustration of a case report of a complex search strategy for humanitarian interventions
Bibliographic record
Abstract
Case reports represent a form of evidence in medicine which detail an unusual or novel clinical case in a short, published report, disseminated for the attention of clinical staff. This form of report is not common outside of clinical practice. We question if the adoption of the 'case report' might also be useful in evidence synthesis. This where the case represents a challenge in undertaking evidence synthesis and the report details not only the resolution but also shows the working to resolve the challenge. Our rationale is that methodological responses to problems arising in complicated evidence synthesis often go unreported. The risk is that lessons learned in developing evidence synthesis are lost if not recorded. This represents a form of research waste. We suggest that the adoption of the case report format might represent the opportunity to highlight not only a challenge (the case) but a worked example of a possible solution (the report). These case reports would represent a resting place for the case, with notes left behind for future researchers to follow. We provide an example of a case report: a complicated search strategy developed to inform an evidence gap map on the effects of interventions in humanitarian settings on food security outcomes in low and middle-income countries and specific high-income countries. Our report details the solution that we developed (the search strategy). We also illustrate how we conceptualised the search, and the approaches that we tested but rejected, and the ideas that we pursued.
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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 | 0.681 | 0.865 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.035 | 0.053 |
| Open science | 0.016 | 0.020 |
| Research integrity | 0.041 | 0.035 |
| Insufficient payload (model declined to judge) | 0.012 | 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, 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".