Pain and dyspnea control in cancer patients of an urgency setting: nursing intervention results
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND OBJECTIVES: To outline best practices guidelines to control pain and dyspnea of cancer patients in an urgency setting. CONTENTS: PI[C]O question, with resource to EBSCO (Medline with Full Text, CINAHL, Plus with Full Text, British Nursing Index), retrospectively from September 2009 to 2014 and guidelines issued by reference entities: Oncology Nursing Society (2011), National Comprehensive Cancer Network (2011; 2014) and Cancer Care Ontario (2010), with a total of 15 articles. The first stage for adequate symptoms control is systematized evaluation. Pharmacological pain control should comply with the modified analgesic ladder of the World Health Organization, including titration, equianalgesia, opioid rotation, administration route, difficult to control painful conditions and adverse effects control. Oxygen therapy and noninvasive ventilation are control modalities of some situations of dyspnea, where the use of diuretics, bronchodilators, steroids, benzodiazepines and strong opioids are effective strategies. Non-pharmacological measures: psycho-emotional support, hypnosis, counseling/training/instruction, therapeutic adherence, music therapy, massage, relaxation techniques, telephone support, functional and respiratory reeducation equally improve health gains. CONCLUSION: Cancer pain and dyspnea control require comprehensive and multimodal approach. Implications for nursing practice: best practice guidelines developed based on scientific evidence may support clinical decision-making with better quality, safety and effectiveness.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".