The International Sepsis Forum's controversies in sepsis: how will sepsis be treated in 2051?
Bibliographic record
Abstract
Sepsis, the life-threatening illness that arises from innate immunity to overwhelming infection, is treated symptomatically at the start of the 21st century. Looking ahead 50 years, one can perhaps foresee profound changes in the way we manage this disorder. A shift from a focus on eradicating micro-organisms as universally inimical to one on supporting optimal host–microbial homeostasis will have a profound impact on how we treat infection, and will relegate antibiotics to a small, adjuvant role. Probiotic therapy may well be as important as antibiotic therapy. Resuscitation strategies will support microvascular flow rather than systemic pressure. Rapid genetic profiling will permit pre-emptive gene therapy for some, and titration of specific therapies directed against fundamental intracellular processes in others. We will treat diseases, not syndromes, and guide therapy by molecular staging. A fanciful victim of sepsis in 2051 illustrates how future treatments might transform sepsis from a prolonged and morbid illness to a rapidly reversed acute disease.
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 | 0.040 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.034 | 0.044 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".