A Multicenter Case-Control Study on Predictive Factors Distinguishing Childhood Leukemia From Juvenile Rheumatoid Arthritis
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Bibliographic record
Abstract
OBJECTIVE: Acute lymphocytic leukemia (ALL) often presents with musculoskeletal concerns such as pain or swelling, even before appearance of blasts in the peripheral blood. Such presentation may lead to misdiagnosis of a child with juvenile rheumatoid arthritis (JRA). This study was designed to identify the predictive factors for leukemia using basic clinical and laboratory information. METHODS: A retrospective chart review was performed using a simple questionnaire to compare the clinical and laboratory findings present during the initial visit to a pediatric rheumatology clinic for 277 children who were ultimately diagnosed with either JRA (n = 206) or ALL (n = 71). Sensitivity and specificity analysis of a variety of parameters, both singly and in combination, was performed to identify predictive value for ALL. RESULTS: The majority (75%) of children with ALL did not have blasts in the peripheral blood at the time of evaluation by pediatric rheumatologists. In children presenting with unexplained musculoskeletal complaints, the 3 most important factors that predicted a diagnosis of ALL were low white blood cell count (< 4 x 10(9)/L), low-normal platelet count (150-250 x 10(9)/L), and history of nighttime pain. In the presence of all 3, the sensitivity and specificity for a diagnosis of ALL were 100% and 85%, respectively. Other findings, including antinuclear antibody, rash, and objective signs of arthritis, were not helpful in differentiating between these diagnoses because they occurred at similar rates in both groups. CONCLUSIONS: When a child develops new-onset bone-joint complaints, the presence of subtle complete blood count changes combined with nighttime pain should lead to consideration of leukemia as the underlying cause.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it