Pain after cardiac surgery
Bibliographic record
Abstract
Acute pain is common after cardiac surgery and can keep patients from participating in activities that prevent postoperative complications especially respiratory complications. Accurate assessment and understanding of pain are vital for providing satisfactory pain control and optimizing recovery. This study was performed to find the location, distribution, and intensity of pain in a sample of adult cardiac surgery patients during their postoperative ICU stay. In a prospective study, pain location, distribution (number of pain areas per patient), and intensity (0–10 numerical rating scale) were documented on 250 consecutive adult patients on the first, second and third postoperative day (POD). Patient characteristics (age, sex, size, and body mass index) were analyzed for their impact on pain intensity. There were 140 male and 110 female patients, with a mean ± SD age of 65.7 ± 13.5 years. The maximal pain intensity was significantly higher on POD 1 and 2 (3.7 ± 2 and 3.9 ± 1.9, respectively) and lower on POD 3 (3.2 ± 1.5). The order of overall pain scores among activities ( P < 0.001) from highest to lowest was coughing, moving or turning in bed, getting up, deep breathing or using the incentive spirometer, and resting. After chest tubes were discontinued, patients had lower pain levels at rest ( P = 0.01), with coughing ( P = 0.05). Age and sex was found to have an impact on pain intensity, with patients <60 years old and male patients having a higher pain intensity than older patients on POD 2 (4.7 ± 2.0 vs 3.2 ± 2.4, P = 0.02 and 4.5 ± 2.3 vs 2.9 ± 2.2, respectively). Pain relief is an important outcome of care. A comprehensive, individualized assessment of pain that incorporates activity levels is necessary to promote satisfactory management of pain. We recommend the use of remifentanil infusion for postoperative pain relief in suitable cardiac surgery patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".