Reducing the burden of preventable deaths from sepsis in Canada: A need for a national sepsis action plan
Bibliographic record
Abstract
Sepsis is a global health threat with significant morbidity and mortality. Despite clinical practice guidelines and developed health systems, sepsis is often unrecognized or misdiagnosed, leading to preventable harm. In Canada, sepsis is responsible for 1 in 20 deaths and is a significant driver of health system costs. Despite being a signatory to the World Health Organization's Resolution WHA 70.7, adopted in 2017, Canada has not lived up to its commitment. Many existing sepsis policies were developed in response to a specific tragedy, and there is no national sepsis action plan. In this article, we describe the burden of sepsis, provide examples of existing, context-specific, reactionary sepsis policies, and urge a coordinated, proactive Canadian sepsis action plan to reduce the burden of sepsis.
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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".