The iatrogenic opioid crisis: an example of “institutional corruption of pharmaceuticals”?
Bibliographic record
Abstract
Rationale Prescribed opioids are major contributors to the current international public health opioid crisis. Such iatrogenic calamities usually result from collective decision failures of healthcare organizations rather than solely of individual organizations or professionals. Findings from a system-wide safety analysis of the iatrogenic opioid crisis that includes roles of pertinent healthcare organizations may help avoid or mitigate similar future tragedies. In this exploratory study, we report on such an analysis. Methods Root cause analysis, incorporating recent suggestions for improvement, was used to retrospectively identify possible causal factors from the literature. Based on their mandated roles and potential influences to prevent or mitigate the iatrogenic crisis, relevant organizations were grouped and stratified from most to least influential. Results The analysis identified a chain of multiple interrelated causal factors within and between organizations. The most influential organizations were: pharmaceutical, political and drug regulatory; next: experts and their related societies, and publications. Less influential: accreditation, professional licensing and regulatory, academic and healthcare funding bodies. Collectively, their views and decisions influenced prescribing practices of frontline healthcare professionals and advocacy groups. Financial associations between pharmaceutical and all other organizations/groups were common. Ultimately, patients were adversely affected. There was a complex association with psychosocial variables. Limitations Our analysis suggests associations not causality. Conclusions The iatrogenic crisis has multiple intricately linked interacting roots. The major catalyst: pervasive pharma-linked financial conflicts of interest (CoIs) involving most of the other healthcare organizations. These extensive financial CoIs were likely triggers for a cascade of erroneous decisions and actions that adversely affected patients. The actions and decisions of pharma ranged from unethical to illegal. The iatrogenic opioid crisis exemplifies widespread “institutional corruption of pharmaceuticals.”
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".