Proceedings of the AMCP Partnership Forum: Breaking the Link Between Pain Management and Opioid Use Disorder
Bibliographic record
Abstract
Prescription drug misuse and abuse, especially with opioid analgesics, is the fastest growing drug problem in the United States. Addressing this public health crisis demands the coordinated efforts and actions of all stakeholders to establish a process of improving patient care and decreasing misuse and abuse. On September 9, 2014, the Academy of Managed Care Pharmacy (AMCP) convened a meeting of multiple stakeholders to recommend activities and programs that AMCP can promote to improve pain management, prevent opioid use disorder (OUD), and improve medication-assisted treatment outcomes. The speakers and panelists recommended that efforts to improve pain management outcomes and reduce the potential for OUD should rely on demonstrated evidence and best practices. It was recommended that AMCP promote a more holistic and evidence-based approach to pain management and OUD treatment that actively engages the patient in the decision-making process and includes care coordination with medical, pharmacy, behavioral, and mental health aspects of organizations, all of which is seamlessly supported by a technology infrastructure. To accomplish this, it was recommended that AMCP work to collaborate with organizations representing these stakeholders. Additionally, it was recommended that AMCP conduct continuing pharmacy education programs, develop a best practices toolkit on pain management, and actively promote quality standards for OUD prevention and treatment.
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.
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 | 0.001 |
| 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".