The need for novel strategies to address postoperative pain associated with cardiac surgery: A commentary and introduction to “SMArTVIEW”
Bibliographic record
Abstract
Background: With coronary heart disease affecting over 2.4 million Canadians, annual cardiac and major vascular surgery rates are on the rise. Unrelieved postoperative pain is among the top five causes of hospital readmission following surgery; little is done to address this postoperative complication. Barriers to effective pain assessment and management following cardiac and major vascular surgery have been conceptualized on patient, health care provider, and system levels.Purpose: In this commentary, we review common patient, health care provider, and system-level barriers to effective postoperative pain assessment and management following cardiac and major vascular surgery. We then outline the SMArTVIEW intervention, with particular attention to components designed to optimize postoperative pain assessment and management.Methods: In conceptualizing the SMArTVIEW intervention design, we sought to address a number of these barriers by meeting the following design objectives: (1) orchestrating a structured process for regular postoperative pain assessment and management; (2) ensuring adequate clinician preparation for postoperative pain assessment and management in the context of virtual care; and (3) enfranchising patients to become active self-managers and to work with their health care providers to manage their pain postoperatively.Conclusions: Innovative approaches to address these barriers are a current challenge to health care providers and researchers alike. SMArTVIEW is spearheading this paradigm shift within clinical research to address barriers that impair effective postoperative pain management by actively engaging health care providers and patients in an accessible format (i.e., digital health solution) to give primacy to the need of postoperative pain assessment and management following cardiac and major vascular surgery.
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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 | 0.026 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.032 | 0.046 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".