Pain and dyspnea control in cancer patients of an urgency setting: nursing intervention results
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it