A Cluster of Symptoms Over Time in Patients With Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Patients with lung cancer present late in the disease and have multiple symptoms. Previous research has shown the symptom cluster of fatigue, weakness, weight loss, appetite loss, nausea, vomiting, and altered taste to be present at time of lung cancer diagnosis. OBJECTIVES: The study determined whether the symptom cluster identified at the time of diagnosis remained 3 and 6 months later, and whether there was a difference in the mean number of symptoms and the mean level of symptom severity over time. The relation of the severity rating for individual symptoms at the time of diagnosis and at 3 and 6 months after diagnosis was examined. Predictors for the number of symptoms and whether the symptom cluster was predictive of death were determined. METHODS: Secondary analysis of an existing data set for 112 patients with newly diagnosed lung cancer assessed at diagnosis and at 3 and 6 months was performed and determined whether they were alive or dead 19 months after diagnosis. RESULTS: The cluster of seven symptoms had internal consistency that remained at 3 and 6 months. The mean symptom severity and the number of symptoms at diagnosis were correlated with later ratings, but decreased in severity over time. A similar decrease in severity rating was seen for the individual symptoms in the cluster. The stage of cancer at diagnosis was the most predictive of the number of cluster symptoms reported. Death 6 to 19 months after diagnosis was predicted by age, stage of cancer at diagnosis, and symptom severity at 6 months. CONCLUSIONS: The symptom cluster remains over the course of lung cancer and is an independent predictor of the patient's death. Symptom severity, the number of symptoms reported, and the severity of the individual symptoms decreased over time. The stage of cancer at diagnosis is the best predictor of symptoms later in the disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".