Improving the quality of systematic reviews of neurological conditions: an assessment of current practice and the development and validation of six new search strategies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our aim was to study the quality of the literature search strategies used in recent systematic reviews and to develop and assess the diagnostic accuracy of six new search strategies (i.e. hedges). METHODS: Six neurological conditions were studied: migraine, stroke, dementia, epileptic seizures, Parkinson's disease and multiple sclerosis. Two reviewers independently assessed the quality of the search strategies used in systematic reviews published in 2015-2016. Complex hedges pertaining to the six conditions for use in Ovid MEDLINE were developed. Their diagnostic accuracy was compared to simple, single-term keyword searches. RESULTS: Almost 60% of quality criteria for the overall literature search strategy used in 182 systematic reviews were not respected. Over 30% of search strategies relied on a single keyword to identify the neurological condition. The sensitivities of our complex hedges amongst 10 311 articles were between 83% and 95%, significantly higher than the simple keyword searches (as low as 48%). The specificities were greater than 97%. CONCLUSIONS: There is great room for improvement in the search strategies used in systematic reviews of neurological conditions. Complex hedges were developed and validated to improve the accuracy of such searches. It is expected that this will lead to higher quality systematic reviews and meta-analyses.
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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 | 0.658 | 0.839 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.059 | 0.037 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".