Application of text mining to the development and validation of a geographic search filter to facilitate evidence retrieval in Ovid <scp>MEDLINE</scp>: An example from the United States
Bibliographic record
Abstract
BACKGROUND: Given the increasing volume of published research in bibliographic databases, efficient retrieval of evidence is crucial and represents an opportunity to integrate novel techniques such as text mining. OBJECTIVES: To develop and validate a geographic search filter for identifying research from the United States (US) in Ovid MEDLINE. METHODS: US and non-US citations were collected from bibliographies of evidence-based reviews. Citations were partitioned by US/non-US status and randomly divided to a training and testing set. Using text mining, common one- and two-word terms in title/abstract fields were identified, and frequencies compared between US/non-US citations. RESULTS: Common US-related terms included (as ratio of frequency in US/non-US citations) US populations and geographic terms [e.g., 'Americans' (15.5), 'Baltimore' (20.0)]. Common non-US terms were non-US geographic terms [e.g., 'Japan' (0.04), 'French' (0.05)]. A search filter was developed with 98.3% sensitivity and 82.7% specificity. DISCUSSION: This search filter will streamline the identification of evidence from the US. Periodic updates may be necessary to reflect changes in MEDLINE's controlled vocabulary. CONCLUSION: Text mining was instrumental to the development of this search filter. A novel technique generated a gold standard set comprising >20,000 citations. This method may be adapted to develop subsequent geographic search filters.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".