Utilisation of search filters in systematic reviews of prognosis questions
Bibliographic record
Abstract
BACKGROUND: Search filters are designed to increase efficiency of information retrieval and can be particularly useful in limiting the large numbers of articles retrieved for systematic reviews (SRs). Existing published prognosis search filters (or hedges) have lower sensitivity and precision values than their therapy counterparts. OBJECTIVES: Taking into account the relatively poor performance of prognosis filters, this study seeks to identify which methods of limiting search results to prognostic studies are most often used by SR teams. METHODS: One hundred and three SRs of prognostic studies published in 2009 and indexed in MEDLINE were retrieved. Each review's search strategy was reviewed and prognosis-related search terms were extracted. RESULTS: Forty-seven of 103 studies used prognosis-related terms to limit the search. Six SRs of 103 did not specify their search terms, and the remaining 50 SRs used content terms only (no terms related to methodology or prognosis). Of the 47 strategies using prognosis-related terms, only six used a published filter. Many SRs used few or poorly selected prognosis-related search terms which are unlikely to provide the sensitivity generally sought for SRs. CONCLUSIONS: Published prognosis search filters are used in only a small minority of prognosis SRs.
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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 | 0.640 | 0.858 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.058 | 0.048 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".