Cocaine and methamphetamine: Pharmacology and dental implications.
Bibliographic record
Abstract
Background: Epidemiological studies have shown that illicit drug use is a persistent and growing problem in our society. Methamphetamine and cocaine are at the top of the list of stimulants commonly abused. There is a need for a disease-targeted approach to the dental management of clients who use these drugs. Methods: A review of the literature was conducted to identify the most up-to-date information for the diagnosis and treatment of dental clients who abuse methamphetamine and cocaine. Databases in the University of Toronto library system were searched for peer-reviewed articles, written in English, and containing data relevant to clinical decision making. Textbooks were chosen from a list of reference materials provided by the National Dental Examination Board. All cited articles were published within the past 5 years. Results and Discussion: There is robust literature on the treatment of individual signs and symptoms associated with methamphetamine and cocaine use. However, there is a dearth of information on the comprehensive, client-centred oral health care that these individuals require. Conclusion: This article reviews the best practices to guide the clinician from the initial oral diagnosis appointment to the maintenance of care, including the pharmacological actions of these drugs of abuse, the specific challenges faced in providing care for this client population, and scientifically based treatment considerations to maximize prognosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".