Comparing the effects of cold therapy and hand and foot massage on postoperative pain among patients with major surgeries
Bibliographic record
Abstract
Background and aims: Surgery is a main treatment for some illnesses. Postoperative pain (POP) is a major postoperative concern for patients and healthcare providers. The present study aimed at comparing the effects of cold therapy and foot and hand massage on POP among patients with major surgeries. Methods: This quality improvement study was conducted in 2019 on ninety patients who underwent thoracoabdominal surgeries in Kashani teaching hospital, Shahrekord, Iran. Participants were randomly assigned to a control, a cold therapy, and a foot and hand massage group through block randomization with a block size of six. Participants in the control group received routine care services, while participants in the cold therapy group received twenty-minute local cold therapy three times a day for 48 hours and participants in the massage group received twenty-minute hand and foot massage three times a day for 48 hours. POP was assessed before and 48 hours after the study intervention using the McGill Pain Questionnaire. The SPSS software was used to analyze the data through the Kolmogorov-Smirnov, Fisher’s exact, chi-square, Kruskal-Wallis, paired-sample t, and Wilcoxon’s sign-ranked tests as well as the one-way analysis of variance. Results: There was no significant difference among the groups respecting the pretest mean score of POP (P > 0.05). The mean score of POP significantly decreased in all groups (P < 0.05) and the amount of decrease in the intervention groups was significantly more than the control group (P < 0.05). Conclusion: Cold therapy and foot and hand massage are effective in significantly reducing POP among patients with major thoracoabdominal surgeries.
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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 | 0.002 |
| 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 | 0.001 |
| 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".