A failed attempt at developing a search filter for systematic review methodology articles in Ovid Embase
Bibliographic record
Abstract
OBJECTIVES: This paper describes the development, execution, and subsequent failure of an attempt to create an Ovid Embase search filter for locating systematic review methodology articles. METHODS: The authors devised a work plan, based on best practices, for search filter development that has been outlined in the literature. Three reference samples were gathered by identifying the OVID Embase records for specific articles that were included in the PubMed Systematic Review Methods subset. The first sample was analyzed to develop a set of keywords and subject headings to include in the search filter. The second and third samples would have been used to calibrate the search filter and to calculate filter sensitivity and precision, respectively. RESULTS: Technical shortcomings, database indexing practices, and the fuzzy nature of keyword terminology relevant to the topic prevented us from designing the search filter. CONCLUSION: Creating a search filter to identify systematic review methodology articles in Ovid Embase is not possible at this time.
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 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 | 0.628 | 0.837 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.051 | 0.031 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".