Dyspnea in patients with stage IV non-small cell lung cancer: a population-based analysis of disease burden and patterns of care
Bibliographic record
Abstract
Background: Patients with metastatic non-small cell lung cancer (NSCLC) experience significant morbidity with dyspnea being a common symptom with a prevalence of 70%. The objective of this study was to determine factors associated with a moderate-to-severe dyspnea score based on the Edmonton Symptom Assessment System (ESAS), as well as resultant patterns of intervention and factors correlated to intervention receipt. Methods: Using health services administrative data, we conducted a population-based study of all patients diagnosed with metastatic NSCLC treated from January 2007 to September 2018 in the province of Ontario. The primary outcomes of interest are the prevalence of moderate-to-severe dyspnea scores, and the receipt of dyspnea-directed intervention. Differences in baseline characteristic between moderate-to-severe dyspnea and low dyspnea score cohorts were assessed by comparative statistics. Predictors of intervention receipt for patients with moderate-to-severe dyspnea scores were estimated using multivariable modified Poisson regression. Results: The initial study cohort included 13,159 patients diagnosed with metastatic NSCLC and of these, 9,434 (71.7%) reported a moderate-to-severe dyspnea score. Compared to patients who did not report moderate-to-severe dyspnea scores, those who reported a moderate-to-severe dyspnea score were more likely to complete a greater number of ESAS surveys, be male, have a higher Elixhauser comorbidity index (ECI) score, and receive subsequent systemic therapy after diagnosis. Most patients with a moderate-to-severe dyspnea score received intervention (96%), of which the most common were palliative care management (87%), thoracic radiotherapy (56%) and thoracentesis (37%). Multivariable regression identified older patients to be less likely to undergo pleurodesis. Thoracentesis was less common for patients living in rural and non-major urban areas, lower income areas, and earlier year of diagnosis. Receipt of thoracic radiotherapy was less common for older patients, females, those with ECI ≥4, patients living in major urban areas, and those with later year of diagnosis. Finally, palliative care referrals were less frequent for patients with ECI ≥4, age 60-69, residence outside of major urban areas, earlier year of diagnosis, and lower income areas. Conclusions: Dyspnea is a prevalent symptom amongst patients with metastatic NSCLC. Subpopulations of patients with moderate-to-severe dyspnea scores were in which inequities may exist in access to care that require further attention and evaluation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".