Bibliographic record
Abstract
Dyspnea is a symptom commonly experienced by cancer patients that causes significant suffering, worsens throughout a patient's disease trajectory, and can be more difficult to manage than other symptoms. Assessment of dyspnea is best accomplished by a subjective description; physiologic measures are only weakly correlated with the patient's experience. It is important to consider a wide range of possible malignant and nonmalignant causes of dyspnea in cancer patients and to correct underlying causes where possible. For patients with refractory dyspnea, opioids are a safe and effective treatment. Benzodiazepines can be considered, but the evidence for their use is weak. Supplemental oxygen is beneficial if patients are hypoxemic, or if they have concurrent chronic obstructive pulmonary disease. Nonpharmacologic strategies such as fan therapy, exercise programs, and pulmonary rehabilitation can also be beneficial. One important diagnosis to consider in all cancer patients is venous thromboembolism. Prompt evaluation and treatment are vital to improving symptoms and outcomes for patients. Although dyspnea is common and potentially debilitating in cancer patients, it can be effectively managed with a structured approach to rule out reversible causes while concurrently treating the patient using appropriate therapeutic strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".