Location and Patterns of Persistent Pain Following Cardiac Surgery
Bibliographic record
Abstract
Objectives: To investigate the specific clinical features of pain following cardiac surgery and evaluate the information derived from different pain measurement tools used to quantify and describe pain in this population. Methods: A prospective observational study was undertaken at two tertiary care hospitals in Australia. Seventy-two (72) adults (mean age, 63±11 years) were included following cardiac surgery via a median sternotomy. Participants completed the Patient Identified Cardiac Pain using numeric and visual prompts (PICP), the McGill Pain Questionnaire-Short Form version 2 (MPQ-2) and the Medical Outcome Study 36-item version 2 (SF-36v2) Bodily Pain domain (BP), which were administered prior to hospital discharge, 4 weeks and 3 months postoperatively. Results: Participants experienced a high incidence of mild (n=45, 63%) to moderate (n=22, 31%) pain prior to discharge, which reduced at 4 weeks postoperatively: mild (n=28, 41%) and moderate (n=5, 7%) pain; at 3 months participants reported mild (n=14, 20%) and moderate (n=2, 3%) pain. The most frequent location of pain was the anterior chest wall, consistent with the location of the surgical incision and graft harvest. Most participants equated “pressure/weight” to “aching” or a “heaviness” in the chest region (based on descriptor of pain in the PICP) and the pain topography was persistent at 4 weeks and 3 months postoperatively. Each pain measurement tool provided different information on pain location, severity and description, with significant change (p<0.005) over time. Conclusion: Mild-to-moderate pain was frequent after sternotomy, improved over time and was mostly located over the incision and mammary (internal thoracic) artery harvest site. Persistent pain at 3 months remained a significant problem in the community within this surgical population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".