Trends In Opioid Prescribing And Self-Reported Pain Among US Adults
Bibliographic record
Abstract
Clinically informed efforts to curb inappropriate opioid prescribing seek to reduce prescribing to adults with lower pain levels that are potentially manageable with alternative therapies. According to the Medical Expenditure Panel Survey, the annual share of US adults who were prescribed opioids decreased from 12.9 percent in 2014 to 10.3 percent in 2016, and the decrease was concentrated among adults with shorter-term rather than longer-term prescriptions. The decrease was also larger for adults who reported moderate or more severe pain (from 32.8 percent to 25.5 percent) than for those who reported less-than-moderate pain (from 8.0 percent to 6.6 percent). In the same period opioids were prescribed to 3.75 million fewer adults reporting moderate or more severe pain and 2.20 million fewer adults reporting less-than-moderate pain. Because the decline in prescribing primarily involved adults who reported moderate or more severe pain, these trends raise questions about whether efforts to decrease opioid prescribing have successfully focused on adults who report less severe pain.
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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 | 0.000 | 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".