Bibliographic record
Abstract
A 10-year-old boy presented with a two-week history of progressive left elbow swelling, stiffness and achy pain. Before symptom onset, he had a minor fall at school and recalled knocking the same elbow against the ground, with no initial problems. He denied any fevers or systemic symptoms. His past history was unremarkable. His family had emigrated from the Philippines in 2005. On examination, the patient appeared well and was afebrile. His left elbow and distal humerus were swollen, with mild diffuse tenderness, and decreased flexion and extension. Warmth and erythema were not detected. The remainder of the examination was normal. Laboratory results showed a normal complete blood count, an erythrocyte sedimentation rate of 44 mm/h (normal 0 mm/h to 10 mm/h) and negative blood cultures after five days. Radiographs of his left elbow showed a large joint effusion and several lucencies in the distal humeral metaphysis (Figure 1). Joint aspiration revealed 5300×106/L white blood cells (24% neutrophils), with negative Gram stain and culture. The patient's bone scan demonstrated increased flow in the distal humerus. His bone biopsy, before antibiotic therapy, was sent for stains and cultures for bacteria, fungi and acid-fast bacilli, which were all negative. Pathology showed a chronic inflammatory infiltrate and no granulomas.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".