Delivering Prolonged Intensive Care to a Non-human Primate: A High Fidelity Animal Model of Critical Illness
Bibliographic record
Abstract
Critical care needs have been rising in recent decades as populations age and comorbidities increase. Sepsis-related admissions to critical care contribute up to 50% of volume and septic shock carries a 35-54% fatality rate. Improvements in sepsis-related care and mortality would have a significant impact of a resource-intensive area of health care delivery. Unfortunately, research has been hampered by the lack of an animal model that replicates the complex care provided to humans in an intensive care unit (ICU). We developed a protocol to provide full ICU type supportive care to Rhesus macaques. This included mechanical ventilation, continuous sedation, fluid and electrolyte management and vasopressor support in response to Ebolavirus-induced septic shock. The animals accurately recapitulated human responses to a full range of ICU interventions (e.g. fluid resuscitation). This model can overcome current animal model limitations by accurately emulating the complexity of ICU care and thereby provide a platform for testing new interventions in critical care and sepsis without placing patients at risk.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".