Long‐term effect of chronic intravenous and inhaled nephrotoxic antibiotic treatment on the renal function of patients with cystic fibrosis
Bibliographic record
Abstract
Cystic fibrosis (CF) patients have numerous infectious exacerbations requiring prolonged antibiotic treatments, some of which are nephrotoxic. Inhaled antibiotics can reach detectable serum levels. We studied the impact of chronic nephrotoxic antibiotic exposure on kidney function in CF population. We collected data retrospectively for 113 adult CF patients followed for 8.5 years. Fifty-seven (50.4%) were males and 56 (49.5%) females (mean age 31.7 years [SD 9.9]), of which 31% had diabetes and 9.7% had hypertension. Over 8.5 years follow up, there were no significant changes in blood urea nitrogen (BUN; P = 0.92) or creatinine (P = 0.2) in the whole group. 22% of patients had ≥1 episodes of acute kidney injury (AKI). The presence of AKI was associated with increased BUN (P = 0.002) and creatinine (P = 0.056) at the end of follow up. Use of intravenous colistin, gentamicin, tobramycin, or vancomycin did not correlate with increased BUN (P = 0.64; P = 0.49; P = 0.51; P = 0.47) or creatinine (P = 0.43; P = 0.49; P = 0.17; P = 0.2) after 8.5 years. Elevated tobramycin peak and trough levels did not correlate with increased BUN or creatinine. Inhaled colistin and gentamicin correlated with increased BUN (P = 0.009; P = 0.02) but not creatinine (P = 0.45; P = 0.46). Inhaled tobramycin did not correlate with increased BUN (P = 0.17) or creatinine (P = 0.58). Only inhaled colistin correlated with AKI episodes (P = 0.03). Chronic inhaled colistin and gentamicin are associated with an increase in BUN but not creatinine at the end of follow up. Inhaled colistin was associated with episodes of AKI. Well-managed intravenous use of nephrotoxic antibiotics in CF population is associated with no major long-term renal toxicity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".