A Multicenter Case-Control Study on Predictive Factors Distinguishing Childhood Leukemia From Juvenile Rheumatoid Arthritis
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".