An assessment of the efficacy of searching in biomedical databases beyond MEDLINE in identifying studies for a systematic review on ward closures as an infection control intervention to control outbreaks
Bibliographic record
Abstract
BACKGROUND: The purpose of our study is to determine the value and efficacy of searching biomedical databases beyond MEDLINE for systematic reviews. METHODS: We analyzed the results from a systematic review conducted by the authors and others on ward closure as an infection control practice. Ovid MEDLINE including In-Process & Other Non-Indexed Citations, Ovid Embase, CINAHL Plus, LILACS, and IndMED were systematically searched for articles of any study type discussing ward closure, as were bibliographies of selected articles and recent infection control conference abstracts. Search results were tracked, recorded, and analyzed using a relative recall method. The sensitivity of searching in each database was calculated. RESULTS: Two thousand ninety-five unique citations were identified and screened for inclusion in the systematic review: 2,060 from database searching and 35 from hand searching and other sources. Ninety-seven citations were included in the final review. MEDLINE and Embase searches each retrieved 80 of the 97 articles included, only 4 articles from each database were unique. The CINAHL search retrieved 35 included articles, and 4 were unique. The IndMED and LILACS searches did not retrieve any included articles, although 75 of the included articles were indexed in LILACS. The true value of using regional databases, particularly LILACS, may lie with the ability to search in the language spoken in the region. Eight articles were found only through hand searching. CONCLUSIONS: Identifying studies for a systematic review where the research is observational is complex. The value each individual study contributes to the review cannot be accurately measured. Consequently, we could not determine the value of results found from searching beyond MEDLINE, Embase, and CINAHL with accuracy. However, hand searching for serendipitous retrieval remains an important aspect due to indexing and keyword challenges inherent in this literature.
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 | 0.625 | 0.873 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.053 | 0.038 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".