Early discharge as a mediator of greater <scp>ICU</scp>‐level care requirements in patients not enrolled on the <scp>AAML</scp>0531 clinical trial: a Children's Oncology Group report
Bibliographic record
Abstract
Previous data suggest that patients enrolled on clinical trials for treatment of cancer have better overall survival than patients who do not enroll; however, short-term outcomes relative to trial enrollment and corresponding mediators have not been assessed. A cohort of pediatric patients with newly-diagnosed acute myeloid leukemia was assembled from the Pediatric Health Information System. We evaluated whether patients not enrolled onto Children's Oncology Group trial AAML0531 had greater intensive care unit (ICU)-level requirements than enrolled patients and whether early discharge after chemotherapy administration mediated this association. Patients not enrolled on AAML0531 were more likely to be discharged early (aOR = 1.40, 95% confidence interval [CI]: 1.02, 1.90) and to require ICU-level care (aOR = 2.00, 95% CI: 1.06, 3.78) than enrolled patients, but early discharge explained only a small proportion (12.3%) of the absolute difference in ICU-level care risk. The direct effect of nonenrollment on the need for ICU-level care was significant (aOR = 1.89, 95% CI: 1.00, 3.94), whereas the indirect effect mediated through early discharge was not (aOR = 1.07, 95% CI: 0.95, 1.19). Factors other than postchemotherapy discharge strategy drive the difference in ICU utilization by trial enrollment status.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".