Prescribing Opioid Analgesics: what Preventive Measures Taken by Doctors to Avoid Their Problematic Use?
Bibliographic record
Abstract
Introduction: Opioid analgesics, whether strong or weak, carry a risk of dependence, abuse, and misuse, influenced by various factors, including individual terrain, environment, properties of the opioids, and pain. The risk of opioid use disorder when taking prescription opioids is approximately 3% over 2 years, highlighting the importance of early detection of patients at risk. Methodology and Objectives: This study is a descriptive and analytical survey that questioned 120 doctors about their opioid prescribing method. It took place over two months using Google Forms software. The goal was to understand how doctors prescribe opioids and what they look for in patients' profiles to detect risks of opioid use disorder. Discussion: Our study highlights that half of doctors do not take the patient's profile into account, even if they have a psychiatric and addictological history. A Canadian study shows that the assessment of the risk of misuse is more frequent than in our study. In addition, Morocco does not have specific recommendations for the risk of addiction, unlike some countries such as Canada. In our study, codeine is widely prescribed, mainly due to doctors' lack of awareness of its addictive potential, followed by Tramadol. However, in the literature, Tramadol and Codeine are more commonly used. Opioids are prescribed for severe pain in most cases, but sometimes for pain without an appropriate indication, such as migraine or fibromyalgia. Studies show high inappropriate use, especially for chronic non-cancer pain. It is crucial not to extend the prescription of opioids beyond three months in the absence of improvement and not to exceed 150 mg of morphine equivalent per day. In addition, it is essential to regularly monitor the pleasant and euphoric effects of opioids, as well as to provide training and information to practitioners while creating a prescribing guide. Conclusion: The dosage of opioids must be adjusted individually according to pain, with ......
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".