Recording Database Searches for Systematic Reviews - What is the Value of Adding a Narrative to Peer-Review Checklists? A Case Study of NICE Interventional Procedures Guidance
Bibliographic record
Abstract
This paper discusses the value of open and transparent methods for recording systematic database search strategies, showing how they have been applied at the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom (UK). Objective – The objectives are to: 1) Discuss the value of search strategy recording methods. 2) Assess any limitations to the practical application of a checklist approach. 3) Make recommendations for recording systematic database searches. Methods – The procedures for recording searches for Interventional Procedures Guidance at NICE were examined. A sample of current methods for recording systematic searches identified in the literature was compared to the NICE processes. The case study analyses the search conducted for evidence about an interventional procedure and shows the practical issues involved in recording the database strategies. The case study explores why relevant papers were not retrieved by a search strategy meeting all of the criteria on the checklist used to peer review it. The evidence was required for guidance on non-rigid stabilisation techniques for the treatment of low back pain. Results – The analysis shows that amending the MEDLINE strategy to make it more sensitive would have increased its yield by 6614 articles. Examination of the search records together with correspondence between the analyst and the searcher reveals the peer reviewer had approved the search because its sensitivity was appropriate for the purpose of producing Interventional Procedures Guidance. The case study demonstrates the limitations of relying on a checklist to ensure the quality of a database search without having any contextual information. Conclusion – It is difficult for the peer reviewer to assess the subjective elements of a search without knowing why it has a particular structure or what the searcher intended. There is a risk that the peer reviewer will concentrate on the technical details, such as spelling mistakes, without having the contextual information. It is beneficial if the searcher records correspondence on key decisions and reports a summary alongside the search strategy. The narrative describes the major decisions that shaped the strategy and gives the peer reviewer an insight into the rationale for the search approach.
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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 | 0.784 | 0.935 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.037 | 0.045 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.031 | 0.044 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".