Investigating the use of peri-operative systemic steroid administration in anterior cervical discectomy and fusion (ACDF) - A systematic review
Bibliographic record
Abstract
Objective: The study aims to analyze the utility of peri-operative systemic intravenous (IV) steroids in mitigating postoperative complications and improving clinical outcomes following anterior cervical discectomy and fusion (ACDF) surgery. Methods: A systematic review was conducted by searching PubMed, Scopus, Cochrane, Web of Science, and Embase databases for studies assessing the role of IV or systemic steroids in ACDF surgery. Data extraction and risk of bias assessment were conducted independently by two reviewers using Covidence, with a third reviewer finalizing the data and settling any conflicts. The systematic review was conducted per PRISMA guidelines and registered on Prospero under the title, Investigating the Effectiveness of Early "SYSTEMIC" (oral or IV) Steroid Administration, within a 24-hour to one-week timeframe post-operatively, in Anterior Cervical Discectomy and Fusion (ACDF): A Systematic Review. The Risk of Bias 2.0 (RoB 2.0) tool was used for clinical trials, and the Newcastle-Ottawa Scale (NOS) was used for retrospective studies. Results: Six studies were included and showed that IV steroids effectively mitigated dysphagia for up to a month, with higher efficacy compared to topical steroids used intraoperatively. However, IV steroids did not significantly impact the incidence of paravertebral swelling. Reductions in dysphonia, pain scores, and airway compromise were observed, but their long-term effects were insignificant. Systemic steroids were also found to delay fusion in some cases for up to six months, but long-term healing and fusion were not significantly impacted. Conclusions: The use of IV steroids in the perioperative period after ACDF surgery is beneficial in mitigating dysphagia, with multiple doses showing long-term effectiveness compared to the transient effects of local steroids used intraoperatively. Patients may experience perceived benefits in terms of airway compromise, pain, and dysphonia without significant systemic complications or fusion failure. However, there is limited evidence regarding the optimal steroid dosing, frequency, and formulation and thus strong recommendations cannot be made.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".