An Examination of Systematic Reviews in the Engineering Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Systematic reviews are a well-established method of research synthesis in medicine and the clinical sciences. Their use in other disciplines has been growing, especially in areas that collaborate with the health sciences. At the authors' institution, requests for help with systematic reviews have become more frequent in recent years across several non-health-science fields. In this paper, the authors explore the use of systematic reviews in the engineering literature, and the need for engineering librarians to be familiar with the conventions of this methodology. This study seeks to answer three questions: 1) are systematic reviews being published in the engineering literature more frequently? 2) is this methodology more prevalent in certain engineering disciplines than in others? and 3) do systematic reviews see greater use than other types of papers? First, the share of papers using this methodology is examined to confirm the authors' impression that the use of this methodology has increased beyond the rate of increase in publications overall. Next, bibliographic records from several abstracting and indexing databases are analyzed to identify the subject areas within engineering in which research synthesis techniques are most prevalent. Citation counts are also analyzed to determine whether systematic reviews are more likely to be used than other papers in the same subject areas, as has been shown to occur in some non-engineering disciplines. Finally, options for librarians to support this type of research synthesis are discussed, including building familiarity with tools such as Rayyan and Covidence, offering expert search guidance through instruction and consultation, and co-authorship.
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.
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 | 0.051 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it