The Contributions of MEDLINE, Other Bibliographic Databases and Various Search Techniques to NICE Public Health Guidance
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 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.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.076 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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