Clinical Handbook of Psychotropic Drugs
Bibliographic record
Abstract
Quick and comprehensive information on psychotropic drugs for adults. - Accurate and up-to-date - Comparison charts help decision-making - Icons and full color - Available in print and online - Downloadable patient info sheets More about this book The Clinical Handbook of Psychotropic Drugs has become a standard reference and working tool for psychiatrists, psychologists, physicians, pharmacists, nurses, and other mental health professionals. - Independent, unbiased, up-to-date -Packed with unique, easy-to-read comparison charts and tables (dosages, side effects, pharmacokinetics, interactions…) for a quick overview of treatment options - Succinct, bulleted information on all classes of medication: on- and off-label indications, (US FDA, Health Canada), recommended dosages, US and Canadian trade names, side effects, interactions, pharmacodynamics, precautions in the young, the elderly, and pregnancy, nursing implications, and much more – all you need to know for each class of drug -Potential interactions and side effects summarized in comparison charts -With instantly recognizable icons and in full color throughout, allowing you to find at a glance all the information you seek -Clearly written patient information sheets available for download as printable PDF files This book is a must for everyone who needs an up-to-date, easy-to-use, comprehensive summary of all the most relevant information about psychotropic drugs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.246 | 0.250 |
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".