The Impact of Using Ice on Quality of Pain Associated with Chest Drain Removal in Postcardiac Surgery Patients: An Evidence-Based Care
Bibliographic record
Abstract
Background: Patients undergoing cardiothoracic surgery require the placement of at least one chest drain. Chest Drain Removal (CDR) has been considered to be a painful event in patient’s postoperative recuperation. Objective: This study aimed to evaluate the impact of using ice on quality of pain associated with CDR in adult patients undergoing cardiac surgery Materials and Methods: This randomized, observer-blind, crossover trial was done on 51 post-cardiac surgery patients who had two chest drains in the Mashhad Heart Center in Iran. The patients were assigned to ice, placebo, and control groups. Ice and placebo bags were used over the region around the chest drains for 20 minutes prior to CDR. The quality of pain was assessedviaShort-Form McGill Pain Questionnaire (SF-MPQ) before and after CRT. The data were analyzed through the SPSS software using ANOVA, Kruskal-Wallis, and Chi-square tests. Results: The study findings revealed that the three groups were not significantly different regarding pain quality before CDR (p=0.24). However, the ice bag group (4.6±4.4) was significantly different from the placebo (8.1±6.9) and control groups (7.1±5.3) concerning the pain quality score immediately after CDR (p<0.05). The results of chi-square test also showed that the three groups were significantly different regarding “hot-burning” (p=0.009). However, no significant differences were observed with regard to other items of SF-MPQ. Conclusion: The results indicated that ice bag application could be used as an effective, safe, and inexpensive non-pharmacological intervention to reduce patients’ pain and increase their comfort during CDR.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".