An Algorithm to Detect Adverse Drug Reactions in the Neonatal Intensive Care Unit
Why this work is in the frame
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Bibliographic record
Abstract
Critically ill newborns in neonatal intensive care units (NICUs) are at greater risk of developing adverse drug reactions (ADRs). Differentiation of ADRs from reactions associated with organ dysfunction/immaturity is difficult. Current ADR algorithm scoring was established arbitrarily without validation in infants. The study objective was to develop a valid and reliable algorithm to identify ADRs in the NICU. Algorithm development began with a 24-item questionnaire for data collection on 100 previously suspected ADRs. Five pediatric pharmacologists independently rated cases as definite, probable, possible, and unlikely ADRs. Consensus "gold standard" was reached via teleconference. Logistic regression and iterative C programs were used to derive the scoring system. For validation, 50 prospectively collected ADR cases were assessed by 3 clinicians using the new algorithm and the Naranjo algorithm. Weighted kappa and intraclass correlation coefficient (ICC) were used to compare validity and reliability of algorithms. The new algorithm consists of 13 items. Kappa and ICC of the new algorithm were 0.76 and 0.62 versus 0.31 and 0.43 for the Naranjo algorithm. The new algorithm developed using actual patient data is more valid and reliable than the Naranjo algorithm for identifying ADRs in the NICU population. Because of the relatively small and nonrandom samples, further refinement and additional testing are needed.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| 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 it