How well do search filters perform in identifying economic evaluations in MEDLINE and EMBASE
Bibliographic record
Abstract
OBJECTIVES: Health technology assessment (HTA) agencies assessing the cost-effectiveness of healthcare technologies seek evidence from economic evaluations. As well as searching economic evaluation databases, researchers often search MEDLINE and EMBASE, using search filters whose current performance is unclear. We assessed the performance of search filters in identifying economic evaluations from MEDLINE and EMBASE. METHODS: A gold standard of economic evaluations was compiled from National Health Service Economic Evaluation Database (NHS EED) records for 2000, 2003, and 2006. Corresponding records were retrieved in MEDLINE and EMBASE. Search filters were identified from the InterTASC Information Specialists' SubGroup Web site and from Canadian Agency for Drugs and Technologies in Health (CADTH) Information Services. The sensitivity and precision of search filters in retrieving gold standard records from MEDLINE and EMBASE were tested. RESULTS: A total of 2,070 full economic evaluations were identified from NHS EED. Of these, 1,955 records were available in Ovid MEDLINE and 1,873 were available in Ovid EMBASE. Thirteen MEDLINE and eight EMBASE filters were identified. NHS Quality Improvement Scotland (full and brief filters), the NHS EED and Royle and Waugh filters achieved over 0.99 sensitivity in MEDLINE. NHS Quality Improvement Scotland, CADTH, Royle and Waugh, and NHS EED filters achieved greater than 0.99 sensitivity in EMBASE. Filters demonstrated low precision. CONCLUSIONS: This research provided new performance data on search filters to identify economic evaluations in MEDLINE and EMBASE. It demonstrated that highly sensitive economic evaluation filters are available, but that precision is low, yielding perhaps 5 relevant records per 100 records scanned.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, not a consensus.
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".