Developing consensus on core outcome domains for assessing effectiveness in perioperative pain management: results of the PROMPT/IMI-PainCare Delphi Meeting
Bibliographic record
Abstract
ABSTRACT: Postoperative pain management is still insufficient, leading to major deficits, including patient suffering, impaired surgical recovery, long-term opioid intake, and postsurgical chronic pain. Yet, identifying the best treatment options refers to a heterogeneous outcome assessment in clinical trials, not always reflecting relevant pain-related aspects after surgery and therefore hamper evidence synthesis. Establishing a core outcome set for perioperative pain management of acute pain after surgery may overcome such limitations. An international, stepwise consensus process on outcome domains ("what to measure") for pain management after surgery, eg, after total knee arthroplasty, sternotomy, breast surgery, and surgery related to endometriosis, was performed. The process, guided by a steering committee, involved 9 international stakeholder groups and patient representatives. The face-to-face meeting was prepared by systematic literature searches identifying common outcome domains for each of the 4 surgical procedures and included breakout group sessions, world-café formats, plenary panel discussions, and final voting. The panel finally suggested an overall core outcome set for perioperative pain management with 5 core outcome domains: physical function (for a condition-specific measurement), pain intensity at rest, pain intensity during activity, adverse events, and self-efficacy. Innovative aspects of this work were inclusion of the psychological domain self-efficacy, as well as the specific assessment of pain intensity during activity and physical function recommended to be assessed in a condition-specific manner. The IMI-PROMPT core outcome set seeks to improve assessing efficacy and effectiveness of perioperative pain management in any clinical and observational studies as well as in clinical practice.
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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 | 0.461 | 0.408 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".