Comparison of gentamicin dose estimates derived from manual calculations, the Australian ‘Therapeutic Guidelines: Antibiotic’ nomogram and the SeBA‐GEN and DoseCalc software programs
Bibliographic record
Abstract
AIM: To compare gentamicin dose estimates from four predictive methods. METHODS: A retrospective study was conducted, comprising patients at Fremantle Hospital who received gentamicin therapy and had at least one gentamicin serum concentration reported. A manual calculation method, the Australian 'Therapeutic Guidelines: Antibiotic' (TGA) nomogram and the SeBA-GEN and DoseCalc software packages were compared. SeBA-GEN dose estimates were regarded as the reference standard. RESULTS: There were 64 males and 30 females with mean age of 58 +/- 16 years. In patients with moderate renal impairment (CL(Cr) = 30-60 ml min(-1); n = 21), mean dose estimates using DoseCalc and the manual calculation method were comparable to SeBA-GEN but the mean TGA nomogram dose (230 mg; 95% confidence interval 179, 281) was significantly lower than SeBA-GEN (286 mg; 261, 311; P = 0.002; one-way RM anova). In patients with mild renal impairment (CL(Cr) = 60-90 ml min(-1); n = 48), DoseCalc (392 mg; 367, 427) was comparable to SeBA-GEN (377 mg; 362, 392). Although the manual method (341 mg; 306, 376; P = 0.007) and the TGA nomogram (335 mg; 302, 368; P < 0.001) estimates were significantly lower than SeBA-GEN, the practical difference was modest. CONCLUSIONS: SeBA-GEN and DoseCalc are generally comparable for estimation of gentamicin doses in patients with renal impairment. The 'Therapeutic Guidelines: Antibiotic' nomogram is a valid approach to dosage estimation, but only when used in patients with normal renal function. Simple manual calculations are a suitable alternative in patients with renal impairment.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".