Concomitant Ceftriaxone and Intravenous Calcium Therapy in Infants
Bibliographic record
Abstract
OBJECTIVE: To determine if increased mortality could be detected with the administration of ceftriaxone and IV calcium in infants through an analysis of a large repository of electronic health records. METHODS: Patients were split into 3 groups: 1) neonates, 2) infants, and 3) infants <1 year whose age was not specified. Deaths were classified into mutually exclusive categories based on the administration and timing of ceftriaxone and IV calcium. Crude death rates were calculated, and logistic regression modeling was used to calculate adjusted relative odds of death with associated covariates. RESULTS: A total of 259,149 infants were identified. Of 79,038 neonates, the proportion of patients that received ceftriaxone and IV calcium within 48 hours who died was 3.8%, compared with 1.95% (IV calcium), 0.3% (ceftriaxone), 1.54% (IV fluids), and 2.03% (parenteral nutrition). For 102,456 infants, the proportions of deaths were 5.47% (ceftriaxone and IV calcium within 48 hours), 0.45% (IV calcium), 0.15% (ceftriaxone), 0.39% (IV fluids), and 5.5% (parenteral nutrition). Multivariate analysis showed increased odds of death in infants who received ceftriaxone and IV calcium within 48 hours, regardless of age, and propensity score-matched analysis showed a more than 2-fold increased risk for death. CONCLUSIONS: The increased risk for death following ceftriaxone and IV calcium administration was noted not only in neonates, but among older infants as well.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".