Sepsis policy, guidelines and standards in Canada: a jurisdictional scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: To our knowledge, this study is the first to identify and describe current sepsis policies, clinical practice guidelines, and health professional training standards in Canada to inform evidence-based policy recommendations. METHODS AND ANALYSIS: This study will be designed and reported according to the Arksey and O'Malley framework for scoping reviews and the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews. EMBASE, CINAHL, Medline, Turning Research Into Practice and Policy Commons will be searched for policies, clinical practice guidelines and health professional training standards published or updated in 2010 onwards, and related to the identification, management or reporting of sepsis in Canada. Additional sources of evidence will be identified by searching the websites of Canadian organisations responsible for regulating the training of healthcare professionals and reporting health outcomes. All potentially eligible sources of evidence will be reviewed for inclusion, followed by data extraction, independently and in duplicate. The included policies will be collated and summarised to inform future evidence-based sepsis policy recommendations. ETHICS AND DISSEMINATION: The proposed study does not require ethics approval. The results of the study will be submitted for publication in a peer-reviewed journal and presented at local, national and international forums.
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 | 0.186 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.012 |
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".