Observational study to analyze patterns of treatment of breakthrough dyspnea in cancer patients in clinical practice
Bibliographic record
Abstract
INTRODUCTION: Although breakthrough dyspnea is very frequent in cancer patients, there are no precise recommendations for treating it. The main objective of this study was to analyze what treatments are used in clinical practice for the management of breakthrough dyspnea in cancer patients in Spain and the secondary objectives were to describe the characteristics of cancer patients with breakthrough dyspnea and the attributes of the disorder. METHODS: Cancer patients over 18 years of age, with breakthrough dyspnea and a Karnofsky performance score of ≥30, who were treated at departments of oncology in institutes across Spain were included in this cross-sectional observational study. The characteristics of breakthrough dyspnea, history of treatment, anthropometric variables, Mahler dyspnea index, Borg scale, Edmonton Symptoms Assessment Scale, and patient satisfaction with current breakthrough dyspnea treatment were assessed. RESULTS: The mean age of the 149 included patients was 66 years (95% confidence interval: 64.3 to 67.9), and 53 were females (35.6%). The mean breakthrough dyspnea intensity was 5.85 (95% confidence interval 5.48 to 6.22, Borg scale). A total of 55.1% of the first-choice treatments consisted of opioids, followed by oxygen (17.3%). A total of 119 patients (79.9%) received monotherapy for breakthrough dyspnea. Patients presenting with basal dyspnea received oxygen in a greater proportion of cases (21.1% vs 7.4%; p = 0.07). Patients with predictable dyspnea received a greater proportion of opioids (70.9% vs 44.4%; p = 0.01). CONCLUSIONS: Opioids constitute first-line therapy for breakthrough dyspnea in routine clinical practice, though the scientific evidence supporting their use is scarce. Further information derived from controlled clinical trials is needed regarding the comparative efficacy of the different treatments in order to justify their use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".