AUCs and 123s: a critical appraisal of vancomycin therapeutic drug monitoring in paediatrics—authors’ response
Bibliographic record
Abstract
We thank Avedissian et al.1 for their interest in our review.2 We agree with them on the need to minimize vancomycin-induced acute kidney injury (VIKI) in paediatric patients, but reiterate that, at this time, there is no compelling evidence to support AUC-based monitoring as a means to achieve this in children or adults. The fervent certainty with which the correspondents seem to interpret the low-quality and inconsistent evidence supporting AUC-based monitoring belies a confirmation bias that perpetuates eminence-based over evidence-based pharmacotherapy. This is exemplified when the correspondents state ‘Le et al.3 found that trough and AUC were significant predictors of VIKI after multivariable adjustment in paediatrics, reaffirming the importance of AUC to safety.’1,3 An unbiased interpretation of this study, and in fact the correspondents’ statement about it, is that trough and AUC were significant predictors of VIKI. With regards to the degree...
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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 | 0.018 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.031 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".