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Record W303865748

Pagliaros' Comprehensive Guide to Drugs and Substances of Abuse. Second Edition

2011· article· en· W303865748 on OpenAlexvenueaboutno aff
Joseph Joel Jeffries

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2011
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychologyLibrary scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

Drug Abuse Pagliaros' Comprehensive Guide to Drugs and Substances of Abuse. Second Edition Louis A Pagliaro, Ann Marie Pagliaro. Washington (DC): American Pharmacists Association; 2009. 419 p. US$79.00 Reviewer rating: Excellent This book reviews in detail 8 classes of chemicals and 98 individual compounds that are abused by human beings. Of these, 9 are naturally growing consumables, some used for religious purposes, and the rest are licit medications or designer drugs. The aim of this book is to tell readers what they need to know about each compound under the headings: names (generic, brand, chemical, and street); pharmacological classification; brief general overview; dosage; mechanism of action; pharmacodynamics or pharmacokinetics; indications; reasons for use; toxicity; abuse potential; withdrawal syndrome; and overdosage. The authors are a pharmacist and a nurse from the University of Alberta and their knowledge is comprehensive and cogently presented. This may sound like a dry reference book but it is actually an absorbing, educational, and entertaining read because of the interesting information about each substance and the fascinating notes that follow each section. Now for a quiz (answers below): 1. What do Al Sharpton, Bob Hope, Harry Potter, Baby Bad One, Coconut Rabbi, and Ontario Hydro have in common? 2. What do Brooke Shields, Bernice, Rosearme Barr, Casper, Jessica Simpson, and Tony Montana have in common? 3. What do you get if you combine Ecstasy and Viagra? [Warning: Do not try this at home without parental permission.] 4. What do Jack Bauer, Tommy Hilfiger, bin Laden, Aunt Hazel, Rambo, Jerry Springer, and Kermit the Frog have in common? 5. What do Bart Simpson, Alice in Wonderland, Beavis and Butthead, Campbell's Tomato Soup, and Popeye the Sailor Man have in common? I learned many interesting and often important things. They give a 10 000-year history of cannabis,p69 including use by Hindus, Zoroastrians, and Essenes. In 70 AD, Pedacius Dioscorides noted that it reduced sexual drive. Eating hashish was prohibited in the Ottoman Empire in 1378 AD. Black cocaine is cocaine hydrochloride mixed with iron thiocyanate to foil drug detection kits.p97 On the Internet, anyone can learn how to extract dextromethorphan from cough remedies.· 108 One can freeze the fentanyl patch to use the narcotic orally.p 138 Heroin is cut for sale and nowadays has less filler, with 27% availability as against just 7% 30 years ago. …

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.163
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1630.145

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.

Opus teacher head0.020
GPT teacher head0.275
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

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