Testing search strategies for systematic reviews in the <scp>M</scp>edline literature database through <scp>P</scp>ub<scp>M</scp>ed
Bibliographic record
Abstract
BACKGROUND: A high-quality electronic search is essential in ensuring accuracy and completeness in retrieved records for the conducting of a systematic review. OBJECTIVE: We analysed the available sample of search strategies to identify the best method for searching in Medline through PubMed, considering the use or not of parenthesis, double quotation marks, truncation and use of a simple search or search history. METHODS: In our cross-sectional study of search strategies, we selected and analysed the available searches performed during evidence-based medicine classes and in systematic reviews conducted in the Botucatu Medical School, UNESP, Brazil. RESULTS: We analysed 120 search strategies. With regard to the use of phrase searches with parenthesis, there was no difference between the results with and without parenthesis and simple searches or search history tools in 100% of the sample analysed (P = 1.0). The number of results retrieved by the searches analysed was smaller using double quotations marks and using truncation compared with the standard strategy (P = 0.04 and P = 0.08, respectively). CONCLUSIONS: There is no need to use phrase-searching parenthesis to retrieve studies; however, we recommend the use of double quotation marks when an investigator attempts to retrieve articles in which a term appears to be exactly the same as what was proposed in the search form. Furthermore, we do not recommend the use of truncation in search strategies in the Medline via PubMed. Although the results of simple searches or search history tools were the same, we recommend using the latter.
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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 | 0.271 | 0.630 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.052 | 0.063 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.077 | 0.010 |
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".