Trends in prescription of strong opioids for 41–80 year old <scp>N</scp>orwegians, 2005–2010
Bibliographic record
Abstract
BACKGROUND: The use of strong opioids is affected by various influences such as increasing emphasis on adequate pain control and increasing measures to counteract opioid abuse. This study will examine trends of analgesic strong opioid use in an older population. METHODS: The study population includes people aged 41-80 who filled at least one prescription for analgesic strong opioids in 2005-2010. Information was obtained from the Norwegian Prescription Database (NorPD), that is, all prescriptions filled in Norwegian pharmacies. RESULTS: Annual rates per 1000 population of opioid users increased from 8.6/1000 in 2005 to 13.3/1000 in 2010 with the greatest increases for the oldest. Oxycodone showed the highest use in Norway and the greatest increase. Buprenorphine showed little use in 2005 but ranked second highest in 2010 at 3.3/1000. Morphine use remained stable over the years. Mean annual amount in defined daily doses (DDDs)/person decreased in the youngest age groups over 2005-2010 and showed a pattern of decreasing amounts with increasing age. About 20% of the study population received opioids from more than two prescribers and annually 20% received more than one type of opioid. High end users were younger and used more prescribers. CONCLUSIONS: Rate of analgesic strong opioid use, particularly oxycodone and buprenorphine, in this older age group increased annually. The pattern of increasing opioid use is consistent with an increased focus on pain control but may also be influenced by altered reimbursement policies and the new convenient pharmaceutical formulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".