Bibliographic record
Abstract
Key Points Fever, weight loss, rash, diaphoresis, or gastrointestinal symptoms should raise suspicion for systemic diseases such as septic arthritis or autoimmune disease.If patients have undergone recent medication treatment, vaccination, or viral exposure, or have traveled, consider serum sickness–like illness, aseptic necrosis, or viral myositis or arthritis.Pain that disrupts only unpleasant activities (eg, school) or occurs in a nonanatomical distribution should prompt consideration of a functional disorder.Be sure to examine proximally and distally to the site of pain, as referred pain is common in children.Screening laboratory studies should be performed when history and physical examination do not yield a definitive diagnosis or suggest systemic or infectious disease, or if pain lasts beyond the expected duration.The Ottawa criteria to guide radiograph imaging decisions have been validated for ankle and knee pain in children older than 5 years.Magnetic resonance imaging (MRI) is most useful if assessment for joint and soft tissue disease, osteomyelitis, and malignancy is needed, although bone scan may be used instead if the area of concern cannot be sufficiently narrowed by examination or if the need for sedation is prohibitive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.128 | 0.036 |
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".