Pain perception assessment using the short-form McGill pain questionnaire after cardiac surgery
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Bibliographic record
Abstract
Background: Pain management remains an integral part of patient care after cardiac surgery, and it required proper pain assessment. The aim of the study was to assess pain perception using validated Arabic version of the short-form McGill Pain Questionnaire (SF-MPQ) and to identify analgesics prescribing patterns post cardiac surgery. Methods: This is a prospective study conducted in an adult cardiac critical care unit of a tertiary cardiac center from September 2018 to March 2019. The study enrolled 74 patients who underwent cardiac surgical procedures through a median sternotomy. Results: The mean age of our patients was 57 ± 11 years and 47 (63.5%) were males. Patients described post-cardiac surgery pain as heavy ( n = 37; 50%) and tiring-exhausting ( n = 49; 66%), mainly at the site of incision ( n = 20; 27%). Pain intensity at day 1 according to pain rating index (PRI) and numerical rating scale (NRS) was 7 (25 th , 75 th percentiles: 2.8–15) and 6 (3–8), respectively. There was a significant change in pain intensity score between 2 days of assessment (PRI: 7 [2.8–15] vs 5 [2–11] P = 0.010; NRS: 6 (3–8) vs 5 (2–8), P = 0.021]). The most common analgesics prescribed were paracetamol (39%) and a combination of tramadol and paracetamol (33.8%). Conclusion: Pain decreased the second day after cardiac surgery compared to day 1. Paracetamol was the most prescribed analgesic; however, there was an underutilization which might be affected by insufficient pain reporting. Future improvement could focus on multimodal pain management and proper communication of pain experience.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it