Nursing intervention including meditation and physiotherapeutic treatment in post PCI chest pain (non-ischemic)
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of nursing educational interventions on patients with post PCI non-ischemic chest pain.METHODOLOGY: This experimental study was conducted from December 2017 to May 2018 on 100 post PCI patients.Patients who came with post PCI non-ischemic chest pain and on initial screening on the Numerical Rating Scale (NRS) patients scored 5 to 10 were included.Those who had post PCI ischemic chest pain with complications were excluded from the study.The Short McGill Pain Questionnaire (SMPQ) was used to assess the effectiveness of nursing educational interventions.Data analysis was performed using SPSS version 26.Percentages were used for categorical variables, and inferential statistics were calculated using the Mann-Whitney Test.Median and range were calculated at baseline, week two, week four and week six for experimental and non-experimental groups.A P-value of <0.05 was considered significant.RESULTS: This study showed that most participants (74%) were males, and almost all (99%) were married.The P-value is significant at different intervals between the experimental and control groups at two, four and six weeks with P-values <0.001, <0.001, and <0.001, respectively.CONCLUSION: The findings of this study revealed that nursing interventions help to reduce post PCI non-ischemic chest pain levels.This study demonstrates that after nurse-led educational interventions, there were significant differences in scores between interventional and non-interventional groups at different levels after PCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".