Global epidemiology of septic shock: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Septic shock is a life-threatening infection frequently responsible for hospital admissions or may be acquired as nosocomial infection in hospitalized patients with resultant significant morbidity and mortality . There is a dearth of data on a résumé and meta-analysis on the global epidemiology of this potentially deadly condition. Therefore, we propose the first systematic review to synthesize existing data on the global incidence, prevalence and case fatality rate of septic shock worldwide. METHODS: We will include cross-sectional, case-control and cohort studies reporting on the incidence, and case fatality rate of septic shock. Electronic databases including PubMed, Embase, WHO Global Health Library and Web of Science will be searched for relevant records published between 1 January 2000 and 31 August 2019. Independents reviewers will perform study selection and data extraction, as well as assessment of methodological quality of included studies. Appropriate meta-analysis will then be used to pool studies judged to be clinically homogenous. Egger's test and funnel plots will be used to detect publication bias. Findings will be reported and compared by human development level of countries. ETHICS AND DISSEMINATION: Being a review, ethical approval is not required as it was obtained in the primary study which will make up the review. This review is expected to provide relevant data to help in evaluating the burden of septic shock in the general population. The overall findings of this research will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42019129783.
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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 | 0.099 | 0.149 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.059 | 0.007 |
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".