Prevalence of dyspnoea and usage of opioids in managing dyspnoea in advanced cancer patients: a longitudinal observational multi-centre study from India
Bibliographic record
Abstract
Context: Breathlessness is one of the devastating symptoms experienced by patients with advanced cancer and can be very challenging to manage. Objectives: To find the point prevalence of dyspnoea in advanced cancer patients presenting to palliative care out-patient clinics, and the usage of opioids in palliation of dyspnoea. Methods: We conducted a prospective observational study among all consecutive patients presenting to the outpatient clinics of six cancer centres in India from different parts of the country. In addition to routinely documented demographic and clinical data from patient charts, study investigators collected information on the Edmonton Symptom Assessment System, Cancer Dyspnoea Scale (CDS) and European Organization for Research and Treatment of Cancer Quality-of-Life Questionnaire Core 15 Palliative Care. We calculated the prevalence of dyspnoea and documented the usage of opioids in palliation of dyspnoea using tests of differences across patient characteristics. Results: Between May 1, 2019, and April 30, 2020, 5,541 patients were screened for eligibility, and 288 were enrolled (48 patients from each of the six centres). We analysed the data of 288 patients, of which 36.4% had dyspnoea, with 28.5% with moderate to a severe degree (>4/10). Tiredness and loss of appetite were found to have associations with dyspnoea which were statistically significant on multivariate analysis. Standard palliative care management and routine usage of opioids preceded improvement in dyspnoea scores, CDS scores and quality of life scores throughout 7 days. Conclusion: Dyspnoea is a common symptom in advanced cancer patients, presenting to outpatient clinics, and routine documentation of dyspnoea with appropriate usage of opioids helps in mitigation. Key message: The article suggests that breathlessness is a common problem in advanced cancer patients and opioid prescription preceded symptom improvements in such patients.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".