Diagnostic and prognostic accuracy of Protein C in adult patients with sepsis: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Sepsis is a dysregulated host response to infection characterised by activation of proinflammatory and procoagulant mechanisms. Protein C (PC)’s activity as an anticoagulant and antiinflammatory molecule makes it an appealing target for sepsis biomarker studies. To date, there has been no systematic review of PC as a sepsis biomarker. Objectives To evaluate the diagnostic accuracy and prognostic strength of PC as a biomarker for adult sepsis. Methods and analysis Medline, Embase, Cochrane Library, PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) will be searched from inception through 20 January 2021 for prospective observational studies that evaluate the use of PC as a diagnostic or prognostic biomarker for adult sepsis. Title and abstract screening, full-text screening and data extraction will be conducted in duplicate. Risk of bias will be assessed using the Quality Assessment of Diagnostic Accuracy Studies and Quality in Prognostic Studies tools. If sufficient data are available, a meta-analysis will be conducted. The standardised mean difference and 95% CI will be calculated for prognostic and diagnostic studies. If possible, a hierarchical summary receiver operator characteristic curve will be generated to assess overall prognostic and diagnostic biomarker accuracy. I2statistics will be used to assess heterogeneity. Sensitivity analysis will be performed by removing studies with a high risk of bias and re-examining the meta-analysis results. Ethics and dissemination Given this is a systematic review and meta-analysis, there is no requirement for ethics approval. Findings will be disseminated through a peer-reviewed publication and social media. PROSPERO registration number CRD42021229786.
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 | 0.068 | 0.101 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.034 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".