Methodological approaches for developing and reporting living evidence synthesis: a study protocol
Bibliographic record
Abstract
Background : Living evidence (LE) refers to the methodological processes that permit new research findings to be continually incorporated into evidence synthesis. This approach is of great value in the resolution of relevant and rapidly changing clinical questions. To date, the methods to carry out this type of synthesis are not completely defined, and great variability is observed in the approaches used by different groups of authors. Objective: To identify, evaluate and summarise the current methods used for living evidence synthesis Methods: We will conduct a methodological study based on a systematic literature search to identify any type of evidence synthesis such as systematic reviews, network metanalyses and overviews that used “living evidence synthesis” as part of their methods. The search will be conducted in Medline (via PubMed) and Epistemonikos databases. Additionally, we will search websites of the organisations publishing any living evidence synthesis retrieved in the two databases, in order to identify unpublished subsequent reports. Two reviewers will independently assess each article against the selection criteria, extract data on methods and procedures, and assess the methodological quality of each publication. Data will be analysed descriptively.
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 | 0.404 | 0.591 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.095 | 0.033 |
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".