Predictors of dyspnea in patients with advanced cancer
Bibliographic record
Abstract
BACKGROUND: More than 70% of patients with advanced cancer experience dyspnea. Dyspnea is predictive of shorter survival and interferes with quality of life (QOL). The present study aimed to identify predictors of the presence and severity of dyspnea in advanced cancer patients. METHODS: A prospective database collected from patients attending a palliative radiotherapy clinic was analyzed for patient demographics, Edmonton Symptom Assessment System (ESAS) scores, Patient-Reported Functional Status (PRFS), history of smoking and respiratory conditions, pulse oximetry readings, and primary cancer site. Using the ESAS shortness of breath item, dyspnea was classified as mild [1-3], moderate [4-6] or severe [7-10]. Logistic regression analysis and generalized estimating equations (GEEs) were used to identify predictors of the severity of dyspnea and presence of moderate/severe dyspnea (ESAS ≥4) at patients' first visit and over time, respectively. RESULTS: A total of 252 patients with dyspnea data were included (median age 71.3 years, 61.5% male, 44.4% had dyspnea) in a demographic analysis. Multivariable analysis showed liver metastases (P=0.01, OR =2.04), a history of respiratory conditions (P=0.03, OR =2.09) and PRFS ≥3 (P=0.03, OR =1.75) were predictive of the severity of dyspnea at the first visit. Analyzed over time, liver metastases (P=0.02, OR =1.80), lymph node metastases (P=0.02, OR =1.79), a history of respiratory conditions (P=0.006, OR =2.50) and pulse oximetry <90 (P=0.003, OR =3.32) were predictive of greater severity of dyspnea symptoms. Patients with multiple radiation treatments in the thorax region were less likely to have severe dyspnea symptoms over time (P=0.01, OR =0.32). Lung metastases (P=0.04, OR =2.03), a history of respiratory conditions (P=0.01, OR =2.60) and PRFS ≥3 (P=0.009, OR =2.30) were predictive of moderate/severe dyspnea at the first visit. Over time, lymph node metastases (P=0.003, OR =2.51), a history of respiratory conditions (P=0.04, OR =2.37) and pulse oximetry <90 (P=0.0004, OR =5.15) were predictive of moderate/severe dyspnea. CONCLUSIONS: Liver, lung and lymph node metastases, a history of respiratory conditions, pulse oximetry <90 and PRFS ≥3 were predictive of the severity of dyspnea and moderate/severe dyspnea. Physicians should be aware of predictive factors that could lead to dyspnea to promote early intervention for improved patient care and the creation of screening tools for clinical practice.
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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 | 0.000 | 0.002 |
| 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 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".