Introducing the Library of Guidance for Health Scientists (LIGHTS)
Bibliographic record
Abstract
Importance: Improving methodological quality is a priority in the health research community. Finding appropriate methods guidance can be challenging due to heterogeneous terminology, poor indexing in medical databases, and variation in formats. The Library of Guidance for Health Scientists (LIGHTS) is a new searchable database for methods guidance articles. Observations: Journal articles that aim to provide guidance for performing (including planning, design, conduct, analysis, and interpretation), reporting, and assessing the quality of health-related research involving humans or human populations (ie, excluding basic and animal research) are eligible for LIGHTS. A team of health researchers, information specialists, and methodologists continuously identifies and manually indexes eligible guidance documents. The search strategy includes focused searches of specific journals, specialized databases, and suggestions from researchers. A current limitation is that a keyword-based search of MEDLINE (and other general databases) and manual screening of records were not feasible because of the large number of hits (n = 915 523). As of September 20, 2022, LIGHTS included 1246 articles (336 reporting guidelines, 80 quality assessment tools, and 830 other methods guidance articles). The LIGHTS website provides a user-oriented search interface including filters for study type, specific methodological topic, research context, guidance type, and development process of the guidance. Automated matching of alternative methodological expressions (eg, enter loss to follow-up and find articles indexed with missing data) enhances search queries. Conclusions and Relevance: LIGHTS is a peer-supported initiative that is intended to increase access to and use of methods guidance relevant to health researchers, statisticians, methods consultants, methods developers, ethics boards, peer reviewers, journal editors, and funding bodies.
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 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 | 0.203 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads 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".