Unilateral sternoclavicular arthritis: inflammatory arthritis or septic arthritis, that is the question – a case report
Bibliographic record
Abstract
Sternoclavicular (SC) joint inflammatory arthritis and septic arthritis can have very similar presentations and can be indistinguishable if a joint fluid aspiration sample cannot be obtained. Septic arthritis of the SC joint accounts for less than 1% of all joint infections. Diagnosis is usually made on the basis of the clinical history combined with elevated infection markers in the blood, specific imaging findings, and most importantly, a positive joint aspiration bacterial culture. To make a diagnosis of SC joint septic arthritis, a high index of suspicion is generally necessary. We herein present the case of a previously healthy 52-year-old man with a 10-day history of left SC pain who improved transiently with anti-inflammatory oral medication; however, the pain subsequently increased over the next 10 days. Follow-up magnetic resonance imaging of the left SC area revealed fluid in the joint with an abscess adjacent to the joint, which was aspirated, and the sample yielded a positive Streptococcus agalactiae culture. Septic arthritis of the left SC joint was diagnosed, and the patient was treated surgically. This case highlights the initial challenges of distinguishing inflammatory from septic arthritis in joints in which a sample for bacterial culture cannot be easily obtained.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".