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 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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".