Inside “Operation Change Agent”: Mallinckrodt's Plan for Capturing the Opioid Market
Bibliographic record
Abstract
CONTEXT: The United States is deeply entangled in an opioid crisis that began with the overuse of prescription painkillers. At the height of the prescription opioid crisis (2006-2012), Mallinckrodt Pharmaceuticals was the nation's largest opioid manufacturer. This study explores Mallinckrodt's strategies for expanding its market share by promoting a new opioid. METHODS: The authors used the Opioid Industry Document Archive to analyze the incentive structures, sales contests, and rhetorical strategy behind Mallinckrodt's "Operation Change Agent," a campaign to switch patients from OxyContin to Mallinckrodt-manufactured painkillers. A structured search of the archive in October 2022 retrieved 464 documents dated between 2010 and 2020. FINDINGS: The authors identified a range of Mallinckrodt's sales force motivational techniques, including hypertargeting high-decile prescribers, providing free trial kits, using emotion-based language to connect with prescribers, and strategies for opposing prescriber resistance. Throughout, managers used specific incentivization metaphors to frame strategies in terms of sport and ultramarathons. CONCLUSIONS: This research on internal corporate strategy joins the growing challenges to industry claims that opioid sales teams simply educated providers and helped fill existing demand for their products. It has important implications for regulatory policy and consumer protections that can better protect health in the face of competitive market forces.
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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 | 0.001 | 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".