Proven infection-related sepsis induces a differential stress response early after ICU admission
Bibliographic record
Abstract
INTRODUCTION: Neuropeptides arginine-vasopressin (AVP), apelin (APL), and stromal-derived factor-1α (SDF-1α) are involved in the dysfunction of the corticotropic axis observed in septic ICU patients. Study aims were: (i) to portray a distinctive stress-related neuro-corticotropic systemic profile of early sepsis, (ii) to propose a combination data score, for aiding ICU physicians in diagnosing sepsis on admission. METHODS: This prospective one-center observational study was carried out in a medical intensive care unit (MICU), tertiary teaching hospital. Seventy-four out of 112 critically ill patients exhibiting systemic inflammatory response syndrome (SIRS) were divided into two groups: proven sepsis and non sepsis, based on post hoc analysis of microbiological criteria and final diagnosis, and compared to healthy volunteers (n = 14). A single blood sampling was performed on admission for measurements of AVP, copeptin, APL, SDF-1α, adrenocorticotropic hormone (ACTH), cortisol baseline and post-stimulation, and procalcitonin (PCT). RESULTS: Blood baseline ACTH/cortisol ratio was lower and copeptin higher in septic vs. nonseptic patients. SDF-1α was further increased in septic patients vs. normal patients. Cortisol baseline, ACTH, PCT, APACHE II and sepsis scores, and shock on admission, were independent predictors of sepsis diagnosis upon admission. Using the three first aforementioned categorical bio-parameters, a probability score for predicting sepsis yielded an area under the Receiver Operating Curve (ROC) curves better than sepsis score or PCT alone (0.903 vs 0.727 and 0.726: P = 0.005 and P < 0.04, respectively). CONCLUSIONS: The stress response of early admitted ICU patients is different in septic vs. non-septic conditions. A proposed combination of variable score analyses will tentatively help in refining bedside diagnostic tools to efficiently diagnose sepsis after further validation.
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 | 0.000 | 0.002 |
| 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) | 0.002 | 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 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".