Pain characteristics and analgesic intake before and following cardiac surgery
Bibliographic record
Abstract
BACKGROUND: Cardiac surgery is a common intervention that involves several pain-sensitive structures, and intense postoperative pain is a predictor of persistent pain. AIMS: To describe pain characteristics (i.e. intensity, location, interference, relief) and analgesic intake preoperatively and across postoperative days 1 to 4 after cardiac surgery, and to explore associations between postoperative pain and demographic and clinical characteristics. METHODS: Four hundred and sixteen patients (24% women) undergoing elective coronary artery bypass grafting and/or valve surgery were enrolled in a randomized controlled trial. Data were collected using standardized measures including the Brief Pain Inventory-short form. A linear mixed model analysis estimated the impact of sex, age, body mass index, analgesic intake and preoperative pain on postoperative worst pain ratings in the previous 24 hours from postoperative days 1 to 4 prior to discharge RESULTS: Thirty-eight per cent of the cardiac surgery patients reported preoperative pain. Postoperative worst pain remained in the moderate to severe range for the majority of patients across day 1 (85%) to day 4 (57%), mainly around the chest incision area for the majority (70%). Mean oral morphine intake was 17 mg/24 h (day 1: 27mg; day 4: 10mg). Lower age, female sex, preoperative pain and analgesic intake had a statistically significant association with higher postoperative worst pain ratings. CONCLUSION: Study findings demonstrated a high prevalence of moderate to severe pain after cardiac surgery and insufficient analgesic administration. Results indicated that patients were discharged from hospital with unrelieved pain and a potential risk for further postoperative complications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".