Does Patient-reported Dyspnea Reflect Thoracic Disease Characteristics in Patients with Incurable Cancer?
Bibliographic record
Abstract
BACKGROUND/AIM: A considerable proportion of patients with incurable cancer experience dyspnea. This study evaluates associations between the feeling of dyspnea, as quantified by radiotherapy patients scoring their symptoms before palliative treatment with the Edmonton symptom assessment system (ESAS), and potential underlying causes. PATIENTS AND METHODS: Retrospective comparison of the incidence of different parameters that could cause a feeling of dyspnea in two groups, patients with no or minimal dyspnea (ESAS score 0-2) and those with dyspnea scores >2. RESULTS: The mean dyspnea score of all 102 patients was 2.6. Dyspnea scores >2 were present in 68% of patients with lung cancer, 50% of those with breast cancer, 39% of those with prostate cancer and 26% of those with other tumors (p=0.025). Dyspnea scores >2 were also present in 69% of patients with pleural effusion (vs. 40% in patients without pleural effusion), p=0.031. Among patients treated with palliative thoracic radiotherapy, 71% had dyspnea scores >2 (40% if other targets were irradiated), p=0.041. In 13% of patients, anemia and pulmonary comorbidity were the most likely explanation for dyspnea. In 29% the feeling of dyspnea could not be related to objective findings. CONCLUSION: In the majority of patients, the feeling of dyspnea was associated with the presence of thoracic metastases with or without pleural effusion from extrathoracic primary tumors or with a lung cancer diagnosis. A substantial proportion of patients reported dyspnea that could be related neither to cancer burden nor comorbidity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".