Kucers’ the use of antibiotics: A clinical review of antibacterial, antifungal, antiparasitic, and antiviral drugs, seventh edition
Bibliographic record
Abstract
Kucers’ The Use of Antibiotics is the definitive, internationally-authored reference, providing everything that the infectious diseases specialist and prescriber needs to know about antimicrobials in this vast and rapidly developing field. The much-expanded Seventh Edition comprises 4800 pages in 3 volumes in order to cover all new and existing therapies, and emerging drugs not yet fully licensed. Concentrating on the treatment of infectious diseases, the content is divided into four sections - antibiotics, anti-fungal drugs, anti-parasitic drugs, and anti-viral drugs - and is highly structured for ease of reference. Each chapter is organized in a consistent format, covering susceptibility, formulations and dosing (adult and pediatric), pharmacokinetics and pharmacodynamics, toxicity, and drug distribution, with detailed discussion regarding clinical uses - a feature unique to this title. Compiled by an expanded team of internationally renowned and respected editors, with expert contributors representing Europe, Africa, Asia, Australia, South America, the US, and Canada, the Seventh Edition adopts a truly global approach. It remains invaluable for anyone using antimicrobial agents in their clinical practice and provides, in a systematic and concise manner, all the information required when prescribing an antimicrobial to treat infection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".