Characteristics of Criminal Cases against Physicians Charged with Opioid-Related Offenses Reported in the US News Media, 1995-2019
Bibliographic record
Abstract
Abstract Background: Pharmaceutical companies and drug distributors are intensely scrutinized in numerous lawsuits for their role in instigating the opioid epidemic. Many individual physicians have also been held accountable for activities related to prescribing opioid medications. The purpose of this study was to examine the epidemiologic patterns of criminal cases against physicians charged with opioid-related offenses reported in the US news media. Methods: We searched the Nexis Uni® database for news media reports on physicians who had been arrested, indicted or criminally charged for illegally prescribing opioids between January 1995 and December 2019. Data collected from the news media reports include defendant’s age, sex, clinical specialty, type of crime and legal consequences. Results: The annual number of criminal cases against physicians charged with opioid-related offenses reported in the US news media increased from 0 in 1995 to 42 in 2019. Of the 372 physician defendants in these criminal cases, 90.1% were male, 27.4% were 65 years and older, and 23.4% were charged in Florida. Of the 358 physician defendants with known clinical specialty, 245 (68.4%) practiced in internal medicine, family medicine, or pain management. Drug trafficking was the most commonly convicted crime (accounting for 54.2% of all convicted cases), followed by fraud (19.1%), money laundering (11.0%) and manslaughter (5.6%). Of the 244 convicted physicians with known sentences, 85.0% were sentenced to prison with an average prison term of 127.3 ± 120.3 months.Conclusions: The US news media has reported on an increasing number of opioid-related criminal cases against physicians from a wide variety of clinical specialties. The most commonly convicted crime in these cases is drug trafficking, followed by fraud, money laundering, and manslaughter.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".