Systemic inflammatory response syndrome in patients with spinal cord injury: does its presence at admission affect patient outcomes?
Bibliographic record
Abstract
OBJECT: The object in this study was to determine whether the presence of systemic inflammatory response syndrome (SIRS) in patients with traumatic spinal cord injury (SCI) on admission is related to subsequent clinical outcome in terms of length of stay (LOS), complications, and mortality. METHODS: The authors retrospectively reviewed the charts of 193 patients with acute traumatic SCI who had been hospitalized at their institution between 2006 and 2012. Patients were excluded from analysis if they had insufficient SIRS data, a cauda equina injury, a previous SCI, a preexisting neurological condition, or a condition on admission that prevented appropriate neurological assessment. Complications were counted only once per patient and were considered minor if they were severe enough to warrant treatment and major if they were life threatening. Demographics, injury characteristics, and outcomes were compared between individuals who had 2 or more SIRS criteria (SIRS+) and those who had 0 or 1 SIRS criterion (SIRS-) at admission. Multivariate logistic regression (enter method) was used to determine the relative contribution of SIRS+ at admission in predicting the outcomes of mortality, LOS in the intensive care unit (ICU), hospital LOS, and at least one major complication during the acute hospitalization. The American Spinal Injury Association Impairment Scale grade and patient age were included as covariates. RESULTS: Ninety-three patients were eligible for analysis. At admission 47.3% of patients had 2 or more SIRS criteria. The SIRS+ patients had higher Injury Severity Scores (24.3 ±10.6 vs. 30.2 ±11.3) and a higher frequency of both at least one major complication during acute hospitalization (26.5% vs. 50.0%) and a fracture-dislocation pattern of injury (26.5% vs. 59.1%) than the SIRS- patients (p < 0.05 for each comparison). The SIRS+ patients had a longer median hospital stay (14 vs 18 days) and longer median ICU stay (0 vs. 5 days). However, mortality was not different between the groups. Having SIRS on admission predicted an ICU LOS > 10 days, hospital LOS > 25 days, and at least one complication during the acute hospitalization. CONCLUSIONS: A protocol to identify SCI patients with SIRS at admission may be beneficial with respect to preventing adverse outcomes and decreasing hospital costs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".