COVID-19 and jaw osteonecrosis: A systematic review on clinical presentations and treatment outcomes
Bibliographic record
Abstract
Purpose To investigate the association between COVID-19 and aseptic osteonecrosis of the jaw, clinical presentation, treatment, and follow-up outcomes. Background Oral manifestations of COVID-19 are amongst the most important extrapulmonary complications of the virus. Spontaneous osteonecrosis of the jaw following COVID-19 is a rare but concerning condition requiring more attention due to its relationship with the novel virus. Types of Studies Reviewed A systematic review on case series. Methods The current study was conducted under Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors searched PubMed, Scopus, Web of Science, and Google Scholar for studies published up to June 24, 2024, on osteonecrosis of the jaws and SARS-CoV-2 infection. Data on patient demographics, study methods, medical records, interventions, and outcomes were extracted from eligible articles. Results Four datasets and 12 patients were included. The mean age of the patients was 49 years old, with 75 % male predominance.33.3% of patients had comorbidities (DMII and HTN). The prevalence of osteonecrosis was higher in the maxilla (83.3% vs. 16.7% in the mandible). Among the patients, 83.3% had undergone steroid therapy related to the COVID-19 treatment. All patients underwent surgical debridement as their treatment. Seven follow-ups with a mean of 7.2 months were reported and no life-threatening complication was observed. Practical Implications According to our results, SARS-CoV-2 may be associated with osteonecrosis of the jaw. The treatment of choice remains surgical debridement, with favorable long-term outcomes. COVID-19 patients should be closely monitored for such serious manifestations even after full recovery from the disease.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".