Natural Progression of Routine Laboratory Markers following Spinal Trauma: A Longitudinal, Multi-Cohort Study
Bibliographic record
Abstract
ABSTRACT Objective To track and quantify the natural course of hematological markers over the first year following spinal cord injury. Methods Data on hematological markers, demographics, and injury characteristics were extracted from medical records of a clinical trial (Sygen) and an ongoing observational cohort study (Murnau Study). The primary outcomes were concentration/levels/amount of commonly collected hematological markers at multiple time-points. Two-way ANOVA and mixed-effects regression techniques were used to account for the longitudinal data and adjust for potential confounders. Trajectories of hematological markers contained in both data sources were compared using the slope of progression. Results At baseline (≤ 2 weeks post-injury), most hematological markers were at pathological levels, but returned to normal values over the course of six to twelve months post-injury. The baseline levels and longitudinal trajectories were dependent on injury severity. More complete injuries were associated with more pathological values (e.g. hematocrit, ANOVA test; Chisq = 77.10, df = 3, adjusted p-value<0.001, and Chisq = 94.67, df = 3, adjusted p-value<0.001, in the Sygen and Murnau studies, respectively). Comparing the two databases revealed some differences in the hematological markers, which are likely attributable to differences in study design, sample size, and standard of care. Conclusions Due to trauma-induced physiological perturbations, hematological markers undergo marked changes over the course of recovery, from initial pathological levels that normalize within a year. The findings from this study are important as they provide a benchmark for clinical decision making and prospective clinical trials. All results can be interactively explored on the Haemosurveillance website ( https://jutzelec.shinyapps.io/Haemosurveillance/ ). Code availability https://github.com/jutzca/Systemic-effects-of-Spinal-Cord-Injury
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".