Evaluation of the safety of piperacillin/tazobactam use in admitted pediatric patients with cystic fibrosis
Bibliographic record
Abstract
Background: Piperacillin/tazobactam (PT) is a first-line antibiotic for Pseudomonas aeruginosa (PsA) respiratory infections in patients with cystic fibrosis (CF), but increased adverse reactions (ARs) have been reported in these patients. We aimed to determine the incidence of and risk factors for ARs to PT within the pediatric CF population. Methods: We conducted a retrospective analysis of CF patients at a pediatric tertiary care centre who had received PT and compared ARs associated with PT versus other antipseudomonal antibiotics. Results: Of the 26 patients who received PT, the PT AR prevalence was n=7 (27%); 3 patients developed fever and rash, 2 had only fever, 1 had only rash, and 1 had fever, rash, and severe neutropenia. The following variables were associated with fever following PT administration: younger age (8.46 versus 13.15 years, p=0.02), fewer previous admissions for CF pulmonary exacerbation (1.67 versus 7.25, p=0.03), and increased PT dose (386.37 versus 270.73 mg/kg/day, p=0.02). Younger age was also associated with increased overall AR to PT (9.6 versus 13.3 years, p=0.04). Increased PT dose was associated with fever (OR 1.02 (1.00–1.05), p=0.03) and with overall reactions (OR 1.01 (1.00–1.02), p=0.03). Comparing incidence rates of ARs following PT and ticarcillin/clavulanate resulted in a trend toward increased relative reaction to PT, but confidence intervals (CIs) were not significant. Conclusions: We found a high AR rate associated with PT, and an association between increased dose and fever and overall ARs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".