Bibliographic record
Abstract
Musculoskeletal infections in children present a diagnostic challenge because they are difficult to recognize in the early stages of the disease and can be confused with other pathology such as tumors or trauma. The severity of disease may be associated with the primary tissue of involvement with bone greater than joint, greater than muscle, greater than soft tissue. The incidence of musculoskeletal infection is higher in infants and young children, and risk factors include premature birth, umbilical catheterization, urinary tract infection, immunodeficiency, and other preexisting disease. Neonates are at greater risk for infection with less virulent organisms due to immaturity of the immune system. The epidemiology of musculoskeletal infection is evolving, and the incidence of musculoskeletal infections in children, especially gram-positive infections, are increasing. Staphylococcus aureus continues to be the leading cause of musculoskeletal infection in children, and the emergence of resistant bacteria such as methicillin-resistant S. aureus is associated with a higher rate of complications. Atypical infections such as tuberculosis have also shown resurgence in the last few decades, whereas other infections such as Haemophilus influenzae are much less prevalent due to widespread immunization. Recent advances in earlier diagnosis and treatment help to reduce complications. However, even when musculoskeletal infection is successfully treated, there may be significant long-term effects on growth.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".