De-duplication of database search results for systematic reviews in EndNote
Bibliographic record
Abstract
When conducting exhaustive searches for systematic reviews, information professionals search multiple databases with overlapping content [1][2][3][4].They typically remove duplicate records to reduce the reviewers' workload associated with screening titles and abstracts; sometimes the reviewers remove the duplicates.Several articles have been published recently on de-duplication methods.In the authors' opinion, these methods are either very time consuming [5] or impractical, as they require uploading large files to an online platform [6,7].A recent overview article compared existing software programs but found that none was truly satisfactory [8].Unique identifiers for journal articles are digital object identifiers (DOIs) and PubMed IDs (PMIDs).However, these identifiers are not present in every database.When they are present, they often cannot be exported easily.Thus, they cannot be relied upon to identify duplicates.An alternative involves using pagination, because the often large page numbers in scientific journals, in combination with other fields, can serve as a type of unique identifier.However, this is complicated by variations in the way page numbers are stored.Most biomedical databases use a long format (e.g.,
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 | 0.276 | 0.568 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.091 | 0.073 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.104 | 0.012 |
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".