The case for ‘conservative pharmacotherapy’
Bibliographic record
Abstract
In the modern era of rapid advances in the field of antimicrobial 'precision dosing' through therapeutic drug monitoring (TDM), there is growing pressure to adopt new technologies and expand the number of antimicrobials managed with TDM and/or the complexity of TDM methods. For many clinicians, it may seem inevitable that TDM must improve patient outcomes. However, based on the evidence to date, this concept remains largely a hypothesis. Conversely, it is plausible that focusing on TDM may distract from careful clinical monitoring of the patient for efficacy and drug-related toxicities and shift finite resources from other valuable interventions. In this article we make the case for embracing critical appraisal of precision dosing, remaining skeptical until persuaded by compelling evidence, and adopting new technologies only when they have proven their value over competing priorities; that is, we make the case for using 'conservative pharmacotherapy'.
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 | 0.058 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.032 | 0.068 |
| Insufficient payload (model declined to judge) | 0.006 | 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".