The case for ‘conservative pharmacotherapy’—authors’ response
Bibliographic record
Abstract
Dear Editor, We thank Alsultan et al.1 for their interest in our article.2 We reiterate the fundamental premise of our article was that the value of precision dosing for any drug, using therapeutic drug monitoring (TDM) or other new technologies, should be convincingly proven and shown to outweigh competing priorities before these new methods are adopted into routine clinical practice. We are concerned that over reliance on numerical values can lead to unnecessary cascades of care and distract from careful clinical assessment, particularly if achieving a pharmacokinetic/pharmacodynamic (PK/PD) target has not been consistently shown to improve outcomes. We were disappointed that Alsultan et al.1 did not directly address our premise, but rather focused on the value of preclinical data in regulatory approval. Further, they make assertions that are not sufficiently supported, for example the unreferenced statement ‘The targets generated by preclinical studies are reproducible and...
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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 | 0.009 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.073 | 0.083 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".