Protocol for a scoping review of sepsis epidemiology
Bibliographic record
Abstract
INTRODUCTION: Sepsis is a common, life-threatening syndrome of physiologic, pathologic, and biochemical abnormalities that are caused by infection and propagated by a dysregulated immune response. In 2017, the estimated annual incidence of sepsis around the world was 508 cases per 100,000 (95% confidence interval [CI], 422-612 cases per 100,000), however, reported incidence rates vary significantly by country. A scoping review will identify knowledge gaps by systematically investigating the incidence of sepsis. METHODS AND ANALYSIS: This scoping review will be guided by the updated JBI (formerly Joanna Briggs Institute) methodology. We will search the following electronic databases: MEDLINE, EMBASE, CINAHL, and Cochrane Database of Systematic Reviews/Central Register of Controlled Trials. In addition, we will search websites of trial and study registries. We will review titles and abstracts of potentially eligible studies and then full-texts by two independent reviewers. We will include any study that is focused on the incidence of sepsis or septic shock in any population. Data will be abstracted independently using pre-piloted data extraction forms, and we will present results according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols Extension for Scoping Reviews. ETHICS AND DISSEMINATION: The results of this review will be used to create a publicly available indexed and searchable electronic registry of existing sepsis research relating to incidence in neonates, children, and adults. With input from stakeholders, we will identify the implications of study findings for policy, practice, and research. Ethics approval was not required given this study reports on existing literature.
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 | 0.127 | 0.212 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.183 | 0.044 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".