Making preclinical sepsis research stronger, faster, and more responsive to patients
Bibliographic record
Abstract
Making preclinical sepsis research stronger, faster, and more responsive to patients Sepsis represents a significant global burden. The National Preclinical Sepsis Platform (NPSP) is leading vital sepsis research, informing policy, driving innovation, and ultimately saving lives. Sepsis is a devastating condition triggered by the body’s extreme response to an infection. Patients with sepsis are often admitted to an intensive care unit (ICU), where they are given a barrage of life-saving treatments to combat the infection and restore basic organ functions like breathing and circulation. Despite our medical advancements, mortality rates for sepsis remain unacceptably high, with 30-50% of patients dying from this condition. In Canada, sepsis accounts for one in every 18 deaths and imposes a staggering $1.7bn burden on the healthcare system each year.
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 | 0.100 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.041 | 0.024 |
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".