Autonomic Dysfunction and Failed Back Surgery Syndrome: A Systematic Review of Associated Comorbidities
Bibliographic record
Abstract
Background Failed Back Surgery Syndrome (FBSS) involves persistent pain and disability when surgical outcomes do not meet preoperative expectations and represents a significant and continuing burden for patients and providers. This study evaluates the possible role of preoperative autonomic dysfunction as a potential overarching modulator of FBSS through established comorbidities. Methods A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Embase, and Scopus were searched from 2009 to August 2024. Included studies were screened for bias and quality using the QUIPS tool, Newcastle-Ottawa Scale, and USPSTF guidelines. Results A total of 21 studies (16 retrospective, 5 prospective) including over one million patients were analyzed. Chronic opioid therapy, COPD, psychiatric diagnosis, and diabetes emerged as the most prevalent comorbidities possibly associated with FBSS and may share a common underlying mechanism: autonomic dysfunction. Conclusion The findings suggest autonomic dysfunction may represent a physiological framework linking multiple risk factors of FBSS. Causality cannot be inferred from the current evidence, but it supports a hypothesis that autonomic dysfunction potentially modulates surgical outcomes through established comorbidities. Further investigation may guide improved preoperative stratification and treatment strategies. Future prospective studies should assess for diseases related to autonomic dysfunction and physiological evidence of preoperative autonomic imbalance pre-operatively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".