Viral infections in orthopedics: A systematic review and classification proposal
Bibliographic record
Abstract
BACKGROUND: Although the impact of microbial infections on orthopedic clinical outcomes is well recognized, the influence of viral infections on the musculoskeletal system might have been underestimated. AIM: To systematically review the available evidence on risk factors and musculoskeletal manifestations following viral infections and to propose a pertinent classification scheme. METHODS: We searched MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), the Reference Citation Analysis (RCA), and Scopus for completed studies published before January 30, 2021, to evaluate risk factors and bone and joint manifestations of viral infection in animal models and patient registries. Quality assessment was performed using SYRCLE's risk of bias tool for animal studies, Moga score for case series, Wylde score for registry studies, and Newcastle-Ottawa Scale for case-control studies. RESULTS: Six human and four animal studies were eligible for inclusion in the qualitative synthesis. Hepatitis C virus was implicated in several peri- and post-operative complications in patients without cirrhosis after major orthopedic surgery. Herpes virus may affect the integrity of lumbar discs, whereas Ross River and Chikungunya viruses provoke viral arthritis and bone loss. CONCLUSION: Evidence of moderate strength suggested that viruses can cause moderate to severe arthritis and osteitis. Risk factors such as pre-existing rheumatologic disease contributed to higher disease severity and duration of symptoms. Therefore, based on our literature search, the proposed clinical and pathogenetic classification scheme is as follows: (1) Viral infections of bone or joint; (2) Active bone and joint inflammatory diseases secondary to viral infections in other organs or tissues; and (3) Viral infection as a risk factor for post-surgical bacterial infection.
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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 | 0.057 | 0.128 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.060 | 0.041 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".