Delayed Diagnosis of Pediatric Sternoclavicular Joint Infections and Clavicular Osteomyelitis During the COVID-19 Pandemic: A Report of 3 Cases
Bibliographic record
Abstract
Sternoclavicular joint infections and osteomyelitis of the clavicle are extremely rare infections, especially in the pediatric population. Early signs of these infections are nonspecific and can be mistaken for common upper respiratory infections such as COVID-19 and influenza. Rapid diagnosis and treatment are critical for preventing potentially fatal complications such as mediastinitis. We present three cases of sternoclavicular joint infections in the past year during the COVID-19 pandemic. All three patients had delayed diagnoses likely secondary to COVID-19 workup. Each patient underwent surgical irrigation and débridement. Two of three patients required multiple surgeries and prolonged antibiotic courses. Placement of antibiotic-impregnated calcium sulfate beads into the surgical site cleared the infection in all cases where they were used. All three patients made a full recovery; however, the severity of their situations should not be overlooked. Children presenting to the hospital with chest pain, fever, and shortness of breath should not simply be discharged based on a negative COVID-19 test or other viral assays. A higher index of suspicion for bacterial infections such as clavicular osteomyelitis is important. Close attention must be placed on the physical examination to locate potential areas of concentrated pain, erythema, or swelling to prompt advanced imaging if necessary.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".