Do beta-blockers alter dyspnea and fatigue in advanced lung cancer? A retrospective analysis
Bibliographic record
Abstract
INTRODUCTION: Dyspnea is common in lung cancer and may be partially attributable to increased ventilatory drive due to muscle weakness. The sympathetic component of this pathway might be mitigated by β-blockers. METHODS: A retrospective review of new patients with stage III-IV non-small lung cancer or any small cell lung cancer was undertaken to assess the impact of β-blocker use on dyspnea and fatigue. Data were abstracted for clinical characteristics, β-blocker use, and pre-treatment Edmonton Symptom Assessment System dyspnea and fatigue scores. RESULTS: Of 348 patients assessed, 202 met eligibility criteria. The median age was 67, 55.4% were female, 18.8% had chronic obstructive pulmonary disease (COPD), and 5.9% had active coronary artery disease. Over 60% of patients scored 4/10 or higher on their dyspnea and fatigue scores. While dyspnea and fatigue were moderately associated, no association was found between β-blocker use and either symptom. Recorded dosages of β-blockers were low. COPD was associated with dyspnea and fatigue, while anemia was associated with fatigue. CONCLUSIONS: Dyspnea and fatigue are prevalent and increased in the presence of COPD and anemia. No association between β-blocker use and dyspnea or fatigue scores was observed. This may be attributable to inadequate dosing or to retrospective bias.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| 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 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".