The Contributions of MEDLINE, Other Bibliographic Databases and Various Search Techniques to NICE Public Health Guidance
Bibliographic record
Abstract
Abstract Objective – To make recommendations for the National Institute for Health and Care Excellence (NICE) on the factors to consider when choosing databases and search techniques when producing systematic reviews to support public health guidance development. Methods – Retrospective analysis of how the publications included in systematic reviews commissioned by NICE on obesity, spatial planning, and tuberculosis were retrieved. The included publications were checked to see if they were found from searching MEDLINE, another database or through other search techniques. Results – MEDLINE contributed 24.2% of the publications included in the obesity review, none of the publications in the spatial planning review and 72% of those in the tuberculosis review. Other databases accounted for 9.1% of included publications in obesity, 20% in spatial planning and 4% in tuberculosis. Non-database methods provided 42.4% of the included publications in the obesity review, compared to 5% in the spatial planning review and 24% in the tuberculosis review. It was not possible to establish retrospectively how 24.2% of the publications in the obesity review and 75% in the spatial planning review were found. Conclusions – Topic-specific databases and non-database search techniques were useful for tailoring the resources to the review questions. The value of MEDLINE in these reviews was affected by the degree of overlap with clinical topics, the domain of public health, and the need to find grey literature. The factors that NICE considers when planning a systematic search are the multidisciplinary nature of public health and the different types of evidence required.
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 | 0.479 | 0.857 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.117 | 0.093 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".