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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".