Association of post‐treatment longitudinal symptom severity clusters with subsequent survival in oropharyngeal cancer
Bibliographic record
Abstract
BACKGROUND: Patients with cancer often experience multiple symptoms concurrently. We identified patient clusters based on longitudinal symptom severity trajectories in oropharyngeal cancer (OPC) and evaluated the potential clinical utility of this approach. METHODS: A retrospective OPC patient cluster analysis using 6 months of symptom severity data from radiotherapy initiation. The clinico-demographic characteristics and overall survival of patients were compared between clusters. RESULTS: We identified four clusters of patients differing in longitudinal symptom severity. Cluster A (n = 168) included patients with the mildest longitudinal symptoms, cluster B (n = 59) and cluster C (n = 63) were intermediate, and cluster D (n = 30) included patients with the worst symptoms. The clusters differed in their HPV status, ECOG performance status, smoking history, drinking history, treatment modality, and 5-year survival. These clusters separated symptom severity trajectories more distinctly than individual clinico-demographic characteristics. CONCLUSIONS: Early symptom severity trajectory clustering revealed distinct patient clusters that were prognostic of overall survival.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".