The Elevated Serum Level of IFN-<i>γ</i> in Patients with Failed Back Surgery Syndrome Remains Unchanged after Spinal Cord Stimulation
Bibliographic record
Abstract
Objectives. We investigated the influence of spinal cord stimulation (SCS) on IFN-γ, IL-1β, IL-6, TNF-α, IL-10, and TGF-β serum levels in failed back surgery syndrome (FBSS) patients. The study will try to give new insights into the mechanism of SCS action and the role of IFN-γ and other cytokines in neuropathic pain (NP) development. Materials and Methods. Clinical and biochemical assessment was conducted in four groups of patients: group 0 consisted of 24 FBSS patients qualified to SCS therapy, group 1 included 17 patients who were one month after implantation, group 2 featured 12 patients who were 3 months after the implantation, and group C (the control group) with no NP. Clinical status was assessed with the use of Numeric Rating Scale (NRS), the Pain Rating Index of McGill Pain Questionnaire (SF-MPQ), the Oswestry Disability Index (ODI), and Beck Depression Inventory (BDI). The plasma concentrations of IFN-γ were ascertained by an immunoenzymatic method. Results. We found a significant difference between the patients before SCS and controls’ serum level of IFN-γ. Similarly, a significantly higher level of TNF-α and significantly lower level of IL-10 in FBSS patients than controls were observed. The significant differences were not observed between SCS patients 3 months after the procedure and controls’ serum level of IFN-γ and other cytokines. We noticed a positive correlation between IFN-γ concentration with NRS back value before SCS and positive correlation between IFN-γ concentration after SCS with NRS leg value before SCS. Higher IFN-γ concentrations accompanied higher NRS values. Levels of TGF-β and IL-10 may correlate with physical ability and depressive behavior. Conclusions. SCS did not influence serum cytokine levels significantly. Serum concentration of IFN-γ may be recognized as an occasional pain factor because of its significantly higher level in FBSS patients versus controls and higher IFN-γ value accompanying higher pain intensity.
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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".