Features at presentation predict children with acute lymphoblastic leukemia at low risk for tumor lysis syndrome
Bibliographic record
Abstract
BACKGROUND: Tumor lysis syndrome (TLS) is a well-recognized complication of acute lymphoblastic leukemia (ALL). The ability to predict children at differing risk of TLS would be an early step toward risk-based approaches. The objectives of the current study were 1) to describe the prevalence and predictors of TLS in childhood ALL and 2) to develop a sensitive prediction rule to identify patients at lower risk of TLS. METHODS: Health records of children aged </=18 years who were diagnosed with ALL between 1998 and 2004 were reviewed. TLS was defined by the presence of >/=2 laboratory abnormalities occurring in the time frame of interest. Predictors of TLS were determined using univariate and multiple logistic regression analyses. RESULTS: Among 328 patients, 23% met criteria for TLS. Factors predictive of TLS were male sex (odds ratio [OR], 1.8; P = .041), age >/=10 years (OR, 4.5; P < .0001), splenomegaly (OR, 3.3; P < .0001), mediastinal mass (OR, 12.2; P < .0001), T-cell phenotype (OR, 8.2; P < .0001), central nervous system involvement (OR, 2.8; P = .026), lactate dehydrogenase >/=2000 U/L (OR, 7.6; P < .0001), and white blood count (WBC) >/=20 x 10(9)/L (OR, 4.7; P < .0001). Among variables that were available at presentation, multiple regression analysis identified age >/=10 years, splenomegaly, mediastinal mass, and initial WBC >/=20 x 10(9)/L as independent predictors of TLS. When all 4 of those predictors were absent at presentation (n = 114 patients), the negative predictive value of developing TLS was 97%, with a sensitivity of 95%. CONCLUSIONS: Clinical and laboratory features at the time of presentation identified a group of children with ALL at low risk for TLS that may benefit from a risk-stratified approach directed at reduced TLS monitoring and prophylaxis.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".