Removing abuse-prone prescription medication from fueling the national opioid crisis through community engagement and surgeon leadership: results of a local drug take-back event
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To address the national opioid and death from overdose crisis in the United States, take-back programs were created to collect and properly dispose of unused abuse-prone drugs. METHODS: Surgeons at Central Michigan University College of Medicine led a community prescription medication take-back drive, administered surveys, characterized event participant demographics, prescription indications, and type and quantity of medications dropped off for disposal. RESULTS: A total of 74,363 dosing units of unused medication were brought in from the homes of 104 event participants. Returned opioids were often prescribed after surgery. Hydrocodone was collected most. Unused opioids were frequently available in homes with children or youth. Collected opioids and benzodiazepines alone had an estimated trademark retail value of over $20,000. CONCLUSION: This surgeon-led public health initiative helped properly dispose a significant amount of unneeded abuse-prone prescription medicine. It highlighted the presence of excess opioid prescribing in a typical Midwestern community. Issues related to improved physician prescribing, utility of take-back drives, and proper drug disposal to avoid misappropriation and abuse by younger generations are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it