Accuracy of Adverse Event Ascertainment in Clinical Trials for Pediatric Acute Myeloid Leukemia
Bibliographic record
Abstract
PURPOSE: Reporting of adverse events (AEs) in clinical trials is critical to understanding treatment safety, but data on AE accuracy are limited. This study sought to determine the accuracy of AE reporting for pediatric acute myeloid leukemia clinical trials and to test whether an external electronic data source can improve reporting. METHODS: Reported AEs were evaluated on two trials, Children's Oncology Group AAML03P1 and AAML0531 arm B, with identical chemotherapy regimens but with different toxicity reporting requirements. Chart review for 12 AEs for patients enrolled in AAML0531 at 14 hospitals was the gold standard. The sensitivity and positive predictive values (PPV) of the AAML0531 AE report and AEs detected by review of Pediatric Health Information System (PHIS) billing and microbiology data were compared with chart data. RESULTS: Select AE rates from AAML03P1 and AAML0531 arm B differed significantly and correlated with the targeted toxicities of each trial. Chart abstraction was performed on 204 patients (758 courses) on AAML0531. AE report sensitivity was < 50% for eight AEs, but PPV was > 75% for six AEs. AE reports for viridans group streptococcal bacteremia, a targeted toxicity on AAML0531, had a sensitivity of 78.3% and PPV of 98.1%. PHIS billing data had higher sensitivity (> 50% for nine AEs), but lower PPV (< 75% for 10 AEs). Viridans group streptococcal detection using PHIS microbiology data had high sensitivity (92.3%) and PPV (97.3%). CONCLUSION: The current system of AE reporting for cooperative oncology group clinical trials in pediatric acute myeloid leukemia underestimates AE rates. The high sensitivity and PPV of PHIS microbiology data suggest that using external data sources may improve the accuracy of AE reporting.
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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 | 0.212 | 0.704 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".