Development and Evaluation of a Height-Based Tobramycin Initial Dosing Nomogram for the Treatment of Adult Cystic Fibrosis Pulmonary Exacerbations
Bibliographic record
Abstract
ABSTRACT: Tobramycin is widely used to treat pulmonary exacerbations of cystic fibrosis. Height has been previously found to be significantly more predictive of tobramycin pharmacokinetics than body weight. This study aimed to develop a height-based initial dosing nomogram and evaluate its performance in peak concentration (Cmax) precision relative to standard and fixed dosing. Monte Carlo simulations were performed to develop a nomogram representing the doses required to reach Cmax targets at different heights. Cmax data observed at 2 clinical centers [McGill University Health Centre (MUHC) and Institut universitaire de cardiologie et pneumologie de Québec (IUCPQ-UL)] were compared with population-predicted Cmax using the doses derived from the nomogram alongside a fixed dose. Height-based dosing resulted in significantly less variable-predicted Cmax values [coefficient of variation (CV) MUHC = 15.7% and IUCPQ-UL = 10.8%] than the Cmax values observed in clinical practice (CV MUHC = 30.0% and CV IUCPQ-UL = 26.9%) and predicted Cmax values obtained from a fixed dose (CV MUHC = 21.2% and CV IUCPQ-UL = 16.3%). An initial dosing nomogram was developed to help reduce pharmacokinetic variability in the observed Cmax. More precise dosing would allow for better clinical outcomes in adult patients with cystic fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".