Complications Following Percutaneous Epidural Neuroplasty: A Narrative Review of Clinical Evidence and the Rationale for Post-Procedural 6 h Inpatient Monitoring Amid Limited Systematic Data
Bibliographic record
Abstract
Background: Percutaneous epidural neuroplasty (PEN) and related adhesiolysis procedures are widely used for managing chronic spinal pain. Although generally safe, complications—ranging from minor to life-threatening—have been reported. This review aimed to estimate the incidence and characteristics of complications following PEN and to evaluate the medical rationale for post-procedural inpatient monitoring. Methods: We systematically searched PubMed, Embase, and the Cochrane Library for studies published from January 2000 to April 2025 reporting complications associated with PEN. We performed a random-effects meta-analysis on five eligible cohort studies to estimate the pooled complication rate and evaluated heterogeneity. Risk of bias was assessed using the Newcastle–Ottawa Scale. Results: Five cohort studies (n = 1740) were included in the meta-analysis, with a pooled complication rate of 9.0% (95% CI: 4.8–13.1%, I2 = 97.5%). A total of 133 complications were identified from cohort studies and case reports. Mechanical and neurological complications were most common. Serious complications, including hematoma, meningitis, and cardiopulmonary arrest, were concentrated within the first 6 h post-procedure. Conclusions: This meta-analysis highlights a quantifiable risk of complications associated with PEN. Our findings support structured inpatient monitoring during the immediate post-procedural period to enhance safety and outcomes.
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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".