Patient safety and systematic reviews: finding papers indexed in MEDLINE, EMBASE and CINAHL
Bibliographic record
Abstract
OBJECTIVE: To develop search strategies for identifying papers on patient safety in MEDLINE, EMBASE and CINAHL. METHODS: Six journals were electronically searched for papers on patient safety published between 2000 and 2006. Identified papers were divided into two gold standards: one to build and the other to validate the search strategies. Candidate terms for strategy construction were identified using a word frequency analysis of titles, abstracts and keywords used to index the papers in the databases. Searches were run for each one of the selected terms independently in every database. Sensitivity, precision and specificity were calculated for each candidate term. Terms with sensitivity greater than 10% were combined to form the final strategies. The search strategies developed were run against the validation gold standard to assess their performance. A final step in the validation process was to compare the performance of each strategy to those of other strategies found in the literature. RESULTS: We developed strategies for all three databases that were highly sensitive (range 95%-100%), precise (range 40%-60%) and balanced (the product of sensitivity and precision being in the range of 30%-40%). The strategies were very specific and outperformed those found in the literature. CONCLUSION: The strategies we developed can meet the needs of users aiming to maximise either sensitivity or precision, or seeking a reasonable compromise between sensitivity and precision, when searching for papers on patient safety in MEDLINE, EMBASE or CINAHL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.278 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads 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".