Sepsis: Using Computers to Understand a Life-Threatening Condition
Bibliographic record
Abstract
Sepsis is a life-threatening reaction to an infection in which the immune system, which usually helps fight infections, reacts abnormally and can cause organs to stop working. About 20% of deaths worldwide are attributed to sepsis—more than any type of heart disease or cancer. Sepsis can be difficult for doctors to recognize because symptoms start out similar to many other medical conditions, and it is hard to treat because the bodily “malfunctions” that cause sepsis vary between patients. To recognize sepsis early and understand differences between patients, researchers are looking at many parts of the immune system at once, collecting lots of data on patients’ genes and proteins. Computers are used to analyze the data, to identify unique patterns or connections. By doing so, scientists have identified unique groups of sepsis patients that differ in their immune responses. This knowledge can help doctors choose the best treatment for each person and might even help protect people from severe COVID-19 or future pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".