Is HSP-27 an emerging marker of good prognosis in septic shockpatients? A pilot study
Bibliographic record
Abstract
Introduction: Many biomarkers are used to assess the severity of sepsis and septic shock (SS), but none are highly sensitive in predicting outcome. Aim of the research: To estimate the value of serum changes of C-reactive protein, procalcitonin, presepsin, heat shock protein 27 (HSP27) and neutrophil to lymphocyte ratio in assessing the prognosis in patients with SS treated in an intensive care unit. Material and methods: Thirty-seven selected adult patients with SS were included. Serum concentrations of biomarkers were measured at admission and daily for 4 consecutive days (time points T0,T1, T2, T3 and T4 respectively). The mortality rate was determined 28 days after admission. Patients were divided into survivor and non-survivor groups according to their mortality. The differences between the levels of biomarkers at the time points T0 and T4 were analyzed. Results: The mean value of the SOFA score on admission was 11.7 ±2.7, and the APACHE II scale 29.9 ±6.85. Nine patients died. Univariate logistic analysis revealed that changes between T0 and T4 of presepsin, procalcitonin, and HSP27 were associated with prognosis. A multivariate Cox analysis showed that an increase in HSP27 at T4 was the only independent predictor of good prognosis in SS patients. The area under the receiver operating characteristics curve for HSP27 was 0.785. Kaplan-Meier analysis showed that the mortality was lower (p = 0.014) in patients who had an increase in HSP27 at T4 compared to those whose serum HSP27 did not increase at T4. Conclusions: The increase of HSP27 level on the 4th day predicts a favorable outcome in SS patients.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".