Role of Corticosteroids as Adjunctive Therapy in Patients With Odontogenic Cervicofacial Infections: Systematic Review
Bibliographic record
Abstract
ABSTRACT Objective The main aim is to evaluate the efficacy and safety of corticosteroid administration as adjuvant therapy in patients with odontogenic cervicofacial infections. Methods We searched five databases, grey literature and manually reviewed bibliographic references for observational studies. Outcomes included hospital length of stay, intensive care unit admission, reoperation and other postoperative complications. The risk of bias was assessed using the Newcastle‐Ottawa Scale. Meta‐analyses were not performed due to the heterogeneity of the results. Results We included four observational studies. Dexamethasone use reduced the odds of reoperation (odds ratio (OR): 0.9; 95% CI: 0.8–0.98). Additionally, higher doses of dexamethasone were associated with a shorter hospital stay (β: −0.2; 95% CI: −0.3 to −0.07). However, another study reported a longer hospital stay among patients who received corticosteroids than those who did not; p < 0.001. Furthermore, corticosteroid use was associated with lower odds of intensive care unit admission (OR: 0.51; 95% CI: 0.09–2.72); p > 0.05. The only reported adverse event was prolonged hyperglycemia (0.8%). Conclusions The adjuvant use of corticosteroids in managing patients with odontogenic cervicofacial infections has potential beneficial effects and appears safe. However, the evidence remains unclear, and further studies are needed to evaluate potential benefits and harms.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".