Systemic Immune Profile Predicts the Development of Infections in Patients with Spinal Cord Injuries
Bibliographic record
Abstract
Patients with spinal cord injury (SCI) frequently develop infections that may affect quality of life, be life-threatening, and impair their neurological recovery in the acute and subacute injury phases. Therefore, identifying patients with SCI at risk for developing infections in this stage is of utmost importance. We determined the systemic levels of immune cell populations, cytokines, chemokines, and growth factors in 81 patients with traumatic SCI at 4 weeks after injury and compared them with those of 26 age-matched healthy control subjects. Patients who developed infections between 4 and 16 weeks after injury exhibited higher numbers of neutrophils and eosinophils, as well as lower numbers of lymphocytes and eotaxin-1 (CCL11) levels. Accordingly, lasso logistic regression showed that incomplete lesions (American Spinal Injury Association Impairment Scale [AIS] C and D grades), the levels of eotaxin-1, and the number of lymphocytes, basophils, and monocytes are predictive of lower odds for infections. On the other hand, the number of neutrophils and eosinophils as well as, in a lesser extent, the levels of IP-10 (CXCL10), MCP-1 (CCL2), BDNF [brain-derived neurotrophic factor], and vascular endothelial growth factor [VEGF]-A, are predictors of increased susceptibility for developing infections. Overall, our results point to systemic immune disbalance after SCI as predictors of infection in a period when infections may greatly interfere with neurological and functional recovery and suggest new pathways and players to further explore novel therapeutic strategies.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".