Efficacy and Safety of a Digital Tapering Intervention for Patients Prescribed Opioids After Surgery: Protocol for a Prospective Exploratory Cohort Study
Bibliographic record
Abstract
BACKGROUND: More than 300 million surgical procedures are performed worldwide each year, and opioids remain a primary approach for managing acute postoperative pain. Studies have demonstrated that a significant number of patients do not discontinue opioid treatment and continue to use opioids for months or even years after surgery. Tapering and management of prescription opioids is a well-known practice and is a part of the current clinical guidelines on safe prescribing. Every patient should receive thorough monitoring, education, and a tapering plan when prescribed opioids or receiving refills after a prolonged treatment. There are challenges associated with tapering, including close follow-up, patient education, clinician time, and withdrawal safety. The evolution of smartphone app use for follow-up has shown promising results in some fields of medicine, and patients are increasingly interested in this approach. OBJECTIVE: The objective of this study is to investigate the efficacy and safety of Prescriby services, comprising clinician management augmented with digital support, as a tapering intervention in patients after knee or hip replacement surgery. METHODS: Efficacy will be measured in tapers successfully completed, doses successfully lowered during tapering, number of active users, satisfaction with the intervention, and patients successfully remaining off opioid medication at 6 and 12 months after the intervention. Participant safety will be monitored by assessing adverse effects during tapering using the numeric pain rating scale to assess the severity of pain. Participants are recruited via referrals from orthopedic clinics and orthopedic departments in the hospital after surgery during the 6-month study period to the Prescriby clinic, where they will receive a personalized tapering treatment and follow-up with a clinical pharmacist. Despite the existence of numerous clinical guidelines on tapering off dependence-inducing medications, there is limited knowledge of the outcomes of such tapering. RESULTS: This study received funding in February 2024. Data collection for secondary outcomes started in November 2024 and ended in July 2025. Data collection for primary outcomes will start in June 2026 and finish in July 2026. As of manuscript submission in August 2025, 75 patients had been recruited into the study. Analysis of secondary data started in August 2025, and results will be published in the fourth quarter of 2025. Analysis of primary data will start in July 2026, and results will be published in the fourth quarter of 2026. CONCLUSIONS: We anticipate that persistent opioid use in standard treatment could be somewhere in the range of 10% to 15%. We hypothesize that the intervention group will have a lower rate of persistent opioid use. With regard to the secondary outcomes, we estimate that more than 85% of the participants in intervention group will be able to taper completely off their opioid medication and anticipate a high degree of satisfaction with the intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72317.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".