Longitudinal changes in clusters of cancer patients over an 18-month period.
Bibliographic record
Abstract
OBJECTIVE: Cross-sectional studies in cancer have revealed the presence of clusters of symptoms (e.g., gastrointestinal, emotional) and of patients (e.g., low or high levels of symptoms), but not much is known about their longitudinal evolution. In addition, their relationships with medical factors (e.g., cancer sites, treatments) and possible consequences (e.g., functioning) have yet to be established. This prospective study assessed the presence of clusters of patients in 828 participants scheduled to undergo surgery for cancer. METHODS: The patients completed the Hospital Anxiety and Depression Scale, the Insomnia Severity Index, the Multidimensional Fatigue Inventory, the EORTC Quality-of-Life-Questionnaire-C30, and a physical symptoms questionnaire at baseline and 2, 6, 10, 14, and 18 months later. RESULTS: Cluster analyses identified between five and eight clusters of patients depending on the time point. The "Low Symptoms" cluster was the most common (24.8 to 35.0% of the sample), whereas one with predominant nausea and vomiting symptoms was among the least common (1.6 to 3.5%). Significant differences were found between cancer sites, treatment regimens, quality of life, and functioning scores. Prostate cancer patients and those treated by surgery only were overrepresented in the "Low" cluster, whereas breast cancer patients were more likely to fall into the "Moderate - Night Sweats" cluster. Clusters with more severe psychological symptoms were associated with lower functioning and quality of life. CONCLUSIONS: This study revealed distinct clusters of patients that varied in number during cancer treatments. Findings also identified some clusters associated with lower quality of life and functioning, which should receive more clinical attention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".