Impact of preoperative education on pain outcomes after coronary artery bypass graft surgery
Bibliographic record
Abstract
Cardiovascular diseases cause more disability and economic loss in industrialized nations than any other group of diseases. In previous work [Nurs Res 49 (2000a) 1], most coronary artery bypass graft patients (CABG, N=225 ) reported unrelieved pain and received inadequate analgesics. This study proposed to evaluate a preadmission education intervention to reduce pain and related activity interference after CABG surgery. Patients (N=406) were randomly assigned to (a) standard care or (b) standard care+pain booklet group. Data were examined at the preadmission clinic and across days 1-5 after surgery. Outcomes were pain-related interference (BPI-I), pain (MPQ-SF), analgesics (chart), concerns about taking analgesics (BQ-SF), and satisfaction (American Pain Society-POQ). The impact of sex was explored related to primary and secondary outcomes. The intervention group did not have better overall pain management although they had some reduction in pain-related interference in activities ( t(355)=2.54, P<0.01) and fewer concerns about taking analgesics ( F(1,313)=2.7, P<0.05) on day 5. Despite moderate 24-h pain intensity across 5 days, patients in both groups received inadequate analgesics (i.e. 33% prescribed dose). Women reported more pain and pain-related interference in activities than men. The booklet was rated as helpful, particularly by women. In conclusion, the intervention did not result in a clinically significant improvement in pain management outcomes. In future, an intervention that considers sex-specific needs and also involves educating the health professionals caring for these patients may influence these results.
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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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".