Does the speed of sternal retraction during coronary artery bypass graft surgery affect postoperative pain outcomes? A randomized controlled trial protocol
Bibliographic record
Abstract
Background: Chronic pain is a serious health issue impacting both the quality of life and productivity of patients. Chronic post-sternotomy pain (CPSP) is characterized by numbness, severe tenderness on palpation, allodynia, as well as constant pain across the anterior chest wall that can persist for months to years after sternotomy. All patients experience early post-operative pain following coronary artery bypass graft (CABG); unfortunately, approximately 30-40% of CABG patients subsequently develop CPSP. Methods: The current study is a prospective, double-blinded, randomized controlled trial. A sample size of 316 randomly assigned patients (n=158 per group) will provide an 80% power at a 2-sided α of 0.05 to detect a 40% decrease in CPSP incidence at 6 months. Eligible patients scheduled for elective, primary coronary artery bypass graft surgery will be randomly assigned to the CONTROL group, in which sternal retraction is conducted over 30 seconds (as per standard practice); or the SLOW group, in which sternal retraction is achieved over 15 minutes. Surgical and perioperative anesthesia protocols between the two groups are otherwise the same. Our primary outcome is the incidence of CPSP at 6 months. Secondary outcomes are: CPSP incidence at 3 and 12 months, daily sternal incision pain intensity (numeric rating scale (NRS)) at rest and while coughing, and daily analgesic consumption while in hospital and at 7 days postoperatively; pain quality, quality of life, and pain interference with daily function at 3, 6, and 12 months post-operatively. Discussion: Our randomized controlled trial will determine whether retracting the sternum more slowly for exposure of the heart during CABG surgery will decrease the incidence and/or severity of CPSP. ClinicalTrials.gov registration: NCT02697812 (03/03/2016
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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".