Patient clusters based on demographics, clinical characteristics and cancer-related symptoms: A cross-sectional pilot study
Bibliographic record
Abstract
PURPOSE: This study aimed to identify and preliminary validate distinct clusters of patients with cancer based on demographics, clinical characteristics, and symptoms and to inform future research on sample size requirements for achieving sufficient power in clustering analyses. METHODS: This cross-sectional pilot study involved 114 patients with cancer from two hospitals in northern Italy. Data were collected on demographics, clinical characteristics, and 20 symptoms using the Edmonton Symptom Assessment System in October 2022. t-distributed stochastic neighbor embedding (t-SNE) was used to reduce the symptom data and demographics (e.g., age) into two components, which were then clustered using Ward's method. A Monte Carlo simulation was conducted based on the t-SNE components to estimate the sample size needed to achieve 80% power for different cluster solutions (k = 2, 3, 4). RESULTS: Two distinct clusters were identified: Cluster 1 (Higher Symptom Burden Cluster) and Cluster 2 (Lower Symptom Burden Cluster). Cluster 1 patients had a higher prevalence of depression, anxiety, and drowsiness. Monte Carlo simulations indicated that 50 patients per cluster were sufficient for k = 2 clusters to achieve 80% power, whereas 90 patients per cluster were needed for k = 3 clusters and 120 patients per cluster for k = 4 clusters. CONCLUSION: This study identified distinct patient clusters and provided preliminary evidence on the sample size required for clustering analyses in cancer research. Understanding patient clusters enables nurses to provide tailored interventions, potentially improving symptom management and overall patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".