Sickle Cell Disease in Children
Bibliographic record
Abstract
OBJECTIVE: To identify clinical and laboratory features predictive of osteomyelitis in children with sickle cell disease and bony pain. DESIGN: Patients in the case group and participants in the control group were randomized in a 1:3 ratio. SETTING: The Hospital for Sick Children, Toronto, Ontario, Canada. PARTICIPANTS: Patients with sickle cell disease and osteomyelitis (case patients) and patients with sickle cell disease and bony, vaso-occlusive crisis (control patients), 18 years or younger. MAIN OUTCOME MEASURES: Five characteristics (number of painful sites, white blood cell count, swelling of the affected limb[s], and duration of pain and fever before presentation) at the time of presentation to hospital. RESULTS: Data were analyzed for 31 cases and 93 controls. Compared with controls, cases had more days of pain (5 vs 2 days; odds ratio [OR], 1.2; 95% confidence interval [CI], 1.1-1.4 days) and fever (1 vs 0 day; 1.7; 1.2-2.4 days) before presentation. Cases were also more likely to have swelling of the affected limb(s) (71% vs 17%; OR, 11.8; 95% CI, 4.6%-30.0%) and fewer painful sites (1 vs 2; 0.7; 0.5-1.0). On laboratory evaluation, cases had higher white blood cell counts (18.6 vs 15.6/microL; OR, 1.1; 95% CI, 1.0-1.1/microL). Multivariate logistic regression showed that the significant predictors of osteomyelitis were duration of fever (OR, 1.8; 95% CI, 1.2-2.6) and pain (1.2; 1.0-1.4) before presentation and swelling of the affected limb (8.4; 3.5-20.0). The risk of osteomyelitis was decreased if more than 1 painful site was present (OR, 0.7; 95% CI, 0.5-1.0). CONCLUSION: In the clinical scenario of a child with sickle cell disease presenting with bony pain and swelling affecting a single site, with prolonged fever and pain, the physician should consider closer monitoring and investigations to exclude a diagnosis of osteomyelitis.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".