Association between prehospital septic shock hemodynamic parameters improvement and 28-day mortality
Bibliographic record
Abstract
Objective: The early hemodynamic optimization of septic shock patients is a cornerstone of care to hope for a better outcome, e.g., mortality decrease. However, in the prehospital setting, hemodynamic evaluation is restricted to micro and macrocirculatory clinical parameters. This study aims to assess the relationship between micro and macrocirculatory hemodynamic parameters improvement and 30-days mortality among septic shock patients being taken care of for by a mobile intensive care unit (mICU) in the prehospital setting. Methods: We performed a retrospective multicenter study, from January 2015 to November 2019 including septic shock patients requiring pre-hospital mICU intervention. Results: Three hundred thirty-seven patients were analyzed. The mean age was 69 ± 15-years-old and 226 of which 67% were male patients. One hundred thirty-six patients (40%) had previous hypertension. Pulmonary infection was the main cause of septic shock (46%) and 30-days mortality reached 30%. After propensity score analysis, for the macrocirculation: when systolic blood pressure increased by at least 30mmHg the odd ratio (OR) for 30-days mortality was 0.77 [0.65-0.84], when diastolic blood pressure increased by at least 5mmHg, the OR for 30-days mortality was 0.95 [0.88-0.99], when mean blood pressure increased by at least 30%, the OR for 30-days mortality was 0.88 [0.77-0.92] and when the heart rate decreased by at least 30 bpm, the OR for 30-days mortality was 0.62 [0.55-0.76]. For microcirculation, when the mottling score decreased by at least 2 points, the OR for 30-days mortality was 0.83 [0.75-0.91]. Conclusion: In this study, we report that prehospital improvement in micro and macrocirculatory parameters are associated with 30-days mortality rate decrease.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".