Specific plasma biomarker signatures associated with patients undergoing surgery for back pain
Bibliographic record
Abstract
BACKGROUND CONTEXT: Intervertebral disc degeneration (IDD) affects numerous people worldwide. The role of inflammation is increasingly recognized but remains incompletely resolved. Peripheral molecules could access neovascularized degenerated discs and contribute to the ongoing pathology. PURPOSE: To assess a large array of plasma molecules in patients with IDD to identify biomarkers associated with specific spinal pathologies and prognostic biomarkers for the surgery outcome. DESIGN: Prospective observational study combining clinical data and plasma measures. PATIENT SAMPLE: Plasma samples were collected just before surgery. Extensive clinical data (age, sex, smoking status, Modic score, glomerular filtration rate, etc.) were extracted from clinical files from 83 patients with IDD undergoing spine surgery. OUTCOME MEASURES: Recovery 2 months postsurgery as assessed by the treating neurosurgeon. METHODS: Over 40 biological molecules were measured in patients' plasma using multiplex assays. Statistical analyses were performed to identify associations between biological and clinical characteristics (age, sex, Body Mass Index (BMI), smoking status, herniated disc, radiculopathy, myelopathy, stenosis, MODIC score, etc.) and plasma levels of biological molecules. RESULTS: Plasma levels of Neurofilament Light chain (NfL) were significantly elevated in patients with myelopathy and spinal stenosis compared to herniated disc. Plasma levels of C- reactive protein (CRP), Neurofilament Light chain (NfL), and Serum Amyloid A (SAA) were negatively associated, while CCL22 levels were positively associated with an efficient recovery 2 months postsurgery. CONCLUSIONS: Our results show that CRP and CCL22 plasma levels combined with the age of the IDD patient can predict the 2-month postsurgery recovery (Area Under the Curve [AUC]=0.883). Moreover, NfL could become a valuable monitoring tool for patients with spinal cord injuries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".