Post-operative pain following cardiac implantable electronic device implantation: insights from the BRUISE CONTROL trials
Bibliographic record
Abstract
AIMS: Post-operative pain following cardiac implantable electronic device (CIED) insertion is associated with patient dissatisfaction, emotional distress, and emergency department visits. We sought to identify factors associated with post-operative pain and develop a prediction score for post-operative pain. METHODS AND RESULTS: All patients from the BRUISE CONTROL-1 and 2 trials were included in this analysis. A validated Visual Analogue Scale (VAS) was used to assess the severity of pain related to CIED implant procedures. Patients were asked to grade the most severe post-operative pain, average post-operative pain, and pain on the day of the first post-operative clinic. Multivariable regression analyses were performed to identify predictors of significant post-operative pain and to develop a pain-prediction score. A total of 1308 patients were included. Multivariable regression analysis found that the presence of post-operative clinically significant haematoma {CSH; P value < 0.001; odds ratio (OR) 3.82 [95% confidence interval (CI): 2.37-6.16]}, de novo CIED implantation [P value < 0.001; OR 1.90 (95% CI: 1.47-2.46)], female sex [P value < 0.001; OR 1.61 (95% CI: 1.22-2.12)], younger age [<65 years; P value < 0.001; OR 1.54 (95% CI: 1.14-2.10)], and lower body mass index [<20 kg/m2; P value < 0.05; OR 2.05 (95% CI: 0.98-4.28)] demonstrated strong and independent associations with increased post-operative pain. An 11-point post-operative pain prediction score was developed using the data. CONCLUSION: Our study has identified multiple predictors of post-operative pain after CIED insertion. We have developed a prediction score for post-operative pain that can be used to identify individuals at risk of experiencing significant post-operative pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".