Prevention and Management of Posterior Wound Complications Following Oncologic Spine Surgery: Narrative Review of Available Evidence and Proposed Clinical Decision-Making Algorithm
Bibliographic record
Abstract
STUDY DESIGN: Narrative Review. OBJECTIVE: Contextualized by a narrative review of recent literature, we propose a wound complication prevention and management algorithm for spinal oncology patients. We highlight available strategies and motivate future research to identify optimal and individualized wound management for this population. METHODS: We conducted a search of recent studies (2010-2022) using relevant keywords to identify primary literature in support of current strategies for wound complication prevention and management following spine tumor surgery. When primary literature specific to spine tumor cases was not available, data were extrapolated from studies of other spine surgery populations. Results were compiled into a proposed clinical algorithm to guide practice considering available evidence. RESULTS: Based on available literature, we recommend individualized stratification of patients according to identifiable risk factors for wound complication and propose several interventions which might be employed preventatively, including intrawound antibiotic administration, negative pressure wound therapy, and primary flap closure of the surgical wound. Of these, the available evidence, weighing possible risks vs benefits, most strongly favors primary flap closure of surgical wounds, particularly for patients with multiple risk factors. A secondary algorithm to guide management of wound complications is also proposed. CONCLUSIONS: Wound complications such as SSI and dehiscence remain a significant source of morbidity following spine tumor surgery. Triaging patients on an individualized basis according to risk factors for complication may aid in selecting appropriate prophylactic strategies to prevent these complications. Future research in this area is still needed to strengthen recommendations.
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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".