Management of Breathlessness in Palliative Care: Inhalers and Dyspnea—A Literature Review
Bibliographic record
Abstract
INTRODUCTION: Dyspnea is prominently observed in palliative care (PC). Dyspnea can be multifactorial, primarily caused by obstructive or restrictive lung diseases or secondarily induced by various comorbidities. Numerous interventions exist, with route of administration and efficacy requiring further discussion. Despite opioids being the first line of treatment, their adverse effects lead to reluctance on the side of patients to take them, creating limitations in patient management planning. OBJECTIVES: This paper reviews and highlights the role of inhalers for dyspnea management in PC. METHODS: The CINAHL, CENTRAL, and OVID databases were searched for scholarly articles on the role of inhalers in dyspnea management from 1998 to the present. A grey literature Internet search was also performed via Google, the World Health Organization, and CareSearch. Twenty-five articles relevant to the subject at hand were located and summarized. The Cochrane Systematic Reviews of Health Promotion and Public Health Interventions Handbook was consulted for structuring. RESULT: Isolated bronchodilators can be effective in dyspnea management. However, combination with opioids leads to a 52% reduction of dyspnea, demonstrating efficacy of their combined use. There is a role for conventional inhalers not only in patients afflicted with chronic obstructive pulmonary disease, but also in those where obstruction is reversible, and in cases of dyspnea not yet diagnosed. CONCLUSION: Inhalers can be utilized as adjuvant therapy to opioids, to limit opioid use, augment responses to dyspnea, and/or minimize opioid side effects, especially in opioid-naïve patients. Correct administration can increase the efficacy of short-acting beta-agonists, long-acting beta-agonists, short- and long-acting anticholinergic agents, and inhaled corticosteroids, achieving reduction and alleviation of dyspnea.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".