De-Implementing Opioid Use and Implementing Optimal Pain Management Following Dental Extractions (DIODE): Protocol for a Cluster Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Overdose deaths from prescription opioid analgesics are a continuing crisis in the United States. Opioid analgesics are among the most frequently prescribed drugs by dentists. An estimated 5 million people undergo third-molar extractions in the United States each year, resulting in postoperative pain. Studies show that, in most cases, the combination of ibuprofen and acetaminophen is an effective alternative to commonly prescribed opioid analgesics for the management of postextraction pain. Nevertheless, many dentists routinely prescribe opioids after dental extractions. OBJECTIVE: We describe the rationale, design, and methods for a randomized trial of interventions designed to de-implement opioid prescribing by dentists while implementing effective nonopioid analgesics following dental extractions. METHODS: Using a prospective, 3-arm, cluster randomized trial design with dentists as the unit randomized and patient-level prescribing data as the primary outcome, we will compare different strategies to reduce the reliance on opioids and increase the use of alternative pain management approaches utilizing information support tools aimed at both providers and their patients. The study will test the efficacy of 2 interventions to decrease opioid prescribing following dental extractions: clinical decision support with (CDS-E) and without patient education (CDS). Providers will be randomized to CDS, CDS-E, or standard practice. Patient-level outcomes will be determined via review of comprehensive electronic health records. We will compare study arms on differential change in prescribing patterns from pre- to postimplementation of the intervention. The primary outcome of interest is a binary indicator of whether or not the patient received an opioid prescription on the day of the extraction encounter. We will also examine recommendations or prescriptions for nonopioid analgesics, patients' perceptions of shared decision making, and patients' pain experiences following the extraction. RESULTS: The HealthPartners Institutional Review Board approved the study. All study materials including the CDS and patient education materials have been developed and pilot tested, and the protocol has been approved by the National Institute of Dental and Craniofacial Research. The intervention was implemented in February 2020, with 51 dentists who were randomized to 1 of the 3 arms. CONCLUSIONS: If the intervention strategies are shown to be effective, they could be implemented more broadly in dental settings with high levels of opioid prescribing. TRIAL REGISTRATION: ClinicalTrials.gov NCT03584789, https://clinicaltrials.gov/ct2/show/NCT03584789. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24342.
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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 | 0.043 | 0.050 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.099 | 0.015 |
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".