Efficacy of searching in biomedical databases beyond MEDLINE in identifying randomised controlled trials on hyperbaric oxygen treatment
Bibliographic record
Abstract
Introduction: Literature searches are routinely used by researchers for conducting systematic reviews as well as by healthcare providers, and sometimes patients, to quickly guide their clinical decisions. Using more than one database is generally recommended but may not always be necessary for some fields. This study aimed to determine the added value of searching additional databases beyond MEDLINE when conducting a literature search of hyperbaric oxygen treatment (HBOT) randomised controlled trials (RCTs). Methods: This study consisted of two phases: a scoping review of all RCTs in the field of HBOT, followed by a a statistical analysis of sensitivity, precision, 'number needed to read' (NNR) and 'number unique' included by individual biomedical databases. MEDLINE, Embase, Cochrane Central Register of Control Trials (CENTRAL), and Cumulated Index to Nursing and Allied Health Literature (CINAHL) were searched without date or language restrictions up to December 31, 2022. Screening and data extraction were conducted in duplicate by pairs of independent reviewers. RCTs were included if they involved human subjects and HBOT was offered either on its own or in combination with other treatments. Results: Out of 5,840 different citations identified, 367 were included for analysis. CENTRAL was the most sensitive (87.2%) and had the most unique references (7.1%). MEDLINE had the highest precision (23.8%) and optimal NNR (four). Among included references, 14.2% were unique to a single database. Conclusions: Systematic reviews of RCTs in HBOT should always utilise multiple databases, which at minimum include MEDLINE, Embase, CENTRAL and CINAHL.
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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 | 0.513 | 0.830 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.049 | 0.047 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".