Sepsis research in Canada: An environmental scan of sepsis investigators, research, and funding
Bibliographic record
Abstract
Sepsis is the world's second leading cause of mortality. In 2017, the World Health Assembly declared sepsis a global priority and adopted a resolution prompting member states to improve the prevention, recognition, and management of sepsis. This cross-sectional study examines the sepsis research landscape in Canada, including demographics, scope, and funding. Using convenient sampling, sepsis researchers in Canada were asked to complete an online 20-question survey. We also scanned the CIHR funding database from 2012-2022 to quantify national research dollars spent on sepsis-related projects. Quantitative data was summarized using descriptive statistics, and textual descriptions of current sepsis research activities were analyzed thematically. With a response rate of 46% (69 of the 150), respondents were primarily men (n = 46/69, 67%), who identified as White/European (n = 49/69, 71%), and were professors or clinical professors (n = 36/69, 52%). The predominant areas of research focus were identification of sepsis (n = 21/55, 38%) and treatment/management (29/55, 53%) of sepsis, while sepsis prevention (n = 4/55, 7%) and sepsis education (n = 5/55, 9%) garnered less attention. Past 10 years of CIHR funding data revealed that only 0.7% ($85 million) of total funding ($11 billion) was towards sepsis research, of which only 2 were new-investigator awards. This study illustrates the need for improving the diversity of sepsis researchers in Canada; expanding the scope of research to address sepsis prevention, recovery, and education; and increasing overall funding to 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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.036 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".