Skin mottling score and capillary refill time to assess mortality of septic shock since pre-hospital setting
Bibliographic record
Abstract
OBJECTIVES: The early identification of septic shock patients at high risk of poor outcome is essential to early initiate optimal treatments and to decide on hospital admission. Biomarkers are often used to evaluate the severity. In prehospital settings, the availability of biomarkers, such as lactate, is restricted. In this context, clinical tools such as skin mottling score (SMS) and capillary refill time (CRT) are more suitable. In this study, we describe prehospital SMS and CRT's ability to predict mortality of patients with septic shock initially cared in the prehospital setting by a mobile intensive care unit. METHODS: Patients with septic shock who received prehospital medical care admitted to the intensive care unit were retrospectively analyzed. RESULTS: Sixty-three patients were included. The origin of sepsis was mainly pulmonary (67%). Overall mortality reached 36%. No significant difference was observed in the duration of prehospital medical care between alive and deceased patients. Mean prehospital value of SMS was 3 ± 2 and mean prehospital value of CRT was 5 ± 1 s. A significant association was found between mortality and prehospital SMS (p = 0.02, OR[CI95] = 1.50 [1.08-2.15]) and prehospital CRT (p = 0.04, OR[CI95] = 1.53 [1.04-2.37]). After adjusting for confounding factors using propensity score, the relative risk of death was 6.58 for SMS > 2 and 2.03 for CRT > 4 s. CONCLUSION: In this study, we report an association between prehospital SMS and CRT, and mortality of patients with septic shock. SMS and CRT are simple tools that could be used to optimize the triage and to decide early intensive care admission.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".