Bibliographic record
Abstract
BACKGROUND: Failed back surgery syndrome (FBSS) is a chronic pain condition that has considerable impact on the patient and health care system. Despite advances in surgical technology, the rates of failed back surgery have not declined. The factors contributing to the development of this entity may occur in the preoperative, intraoperative, and postoperative periods. Due to the severe pain and disability this syndrome may cause, more radical treatments have been utilized. Recent trials have been published that evaluate the efficacy and cost-effectiveness of therapeutic modalities such as spinal cord stimulation for the management of patients with failed back surgery. REVIEW SUMMARY: This article will describe the epidemiology and etiology of FBSS. The importance of prevention will be emphasized. In those patients with established FBSS, a guide to interdisciplinary evaluation and management will be outlined. Special attention will focus on recent trials that have studied the efficacy of more invasive procedures such as spinal cord stimulation. Finally, a suggested management pathway is presented. CONCLUSION: FBSS is a challenging clinical entity with significant impact on the individual and society. To better prevent and manage this condition, knowledge of the factors contributing to its development is necessary. While research on FBSS has increased in recent years, perhaps the best strategy to reduce incidence and morbidity is to focus on prevention. Patients diagnosed with FBSS should be managed in an interdisciplinary environment. More radical treatments for FBSS have now been extensively studied providing clinicians with much needed evidence on their efficacy. Incorporating these results into our current knowledge provides a basis on which to construct an evidence-based guide on how best to manage patients who suffer from FBSS.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".