Changing patterns in opioid addiction
Bibliographic record
Abstract
OBJECTIVE To evaluate the clinical observation that the number of individuals seeking opioid detoxification from oxycodone was increasing at the Centre for Addiction and Mental Health (CAMH) in Toronto, Ont; and to identify the characteristics of individuals seeking opioid detoxification at CAMH. DESIGN Retrospective analysis of patient health records. SETTING Medical Withdrawal Management Service at CAMH. PARTICIPANTS All patients admitted for opioid detoxification between January 2000 and December 2004. MAIN OUTCOME MEASURES Number of opioid detoxification admissions each year; type, dose, and source of opioids; comorbid problems and symptoms. RESULTS There were 571 opioid detoxification admissions during the 5-year study period. The number of admissions increased steadily over the 5 years; in particular, the number of admissions related to controlled-release oxycodone increased substantially (3.8%, 8.3%, 20.8%, 30.6%, and 55.4% of the total opioid admissions in 2000 to 2004, respectively; χ4[2][1]= 105.5, P < .001). The rates of admissions involving heroin remained low and stable. Use of controlled-release oxycodone was associated with considerably higher doses than use of other prescription opioids was. Physician prescriptions were the source of the prescription opioids for a large percentage of patients, particularly for older patients. Prescription opioid users reported considerable comorbid substance use problems, pain, and psychiatric symptoms. CONCLUSION This study has demonstrated a significant rise in the number of individuals seeking treatment at CAMH for controlled-release oxycodone addiction. The substantial comorbid pain, psychiatric symptoms, and other psychoactive substance use problems in these patients, coupled with the finding that prescriptions were an important source of opioids, highlight the clinical complexities encountered in the treatment of these individuals. Further research examining these complexities and the many possible pathways leading to prescription opioid addiction is required in order to develop effective prevention and treatment strategies. [1]: #ref-2
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".