Elevated Serum Interleukin-17 Levels in Thoracic Trauma Patients with Poor Prognosis: A Prospective Observational Study
Bibliographic record
Abstract
PURPOSE: To measure interleukin (IL)-17 serum levels in thoracic trauma patients and to correlate these levels with other cytokines and with patient prognosis. Methods: This prospective observational study recruited 130 thoracic trauma patients who were admitted to the Zhoupu Hospital Affiliated to Shanghai Medical College of Health June 2020 to April 2022 and 100 healthy volunteers. Patients were divided into two groups based on Injury Severity Score (ISS): ISS<16 (mild/moderate trauma) and ISS ≥16 (severe trauma). Serum IL-17, tumor necrosis factor α (TNF-α), IL-6, IL-1β and C-reactive protein (CRP) levels were measured by enzyme-linked immunosorbent assay. Patients with poor prognosis were defined as those who developed serious complications or died during hospitalization or follow-up. Results: Serum levels of IL-17, TNF-α, IL-6 and IL-1β were significantly elevated in patients with ISS ≥16 (p<0.05). Serum cytokines levels increased within 48 h in both groups and then gradually decreased during subsequent treatment and rehabilitation. Pearson's analysis indicated a positive correlation among IL-17, TNF-α and IL-1β. Serum IL-17 levels in patients with poor prognoses were higher than the patients with good prognoses at all time points (p<0.05). Furthermore, for patients with poor prognoses, the serum IL-17 levels had highest diagnostic value among all the cytokines measured. Logistic regression analysis showed that IL-17 was the risk factor for thoracic trauma patients with poor prognoses. Conclusion: Serum IL-17 levels were significantly elevated in thoracic trauma patients and decreased gradually with rehabilitation. IL-17 was a risk factor for thoracic trauma patients with poor prognoses. This study suggests a new diagnostic and therapeutic target for thoracic trauma 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".