Patient Self-Reports of Symptoms and Clinician Ratings as Predictors of Overall Cancer Survival
Bibliographic record
Abstract
BACKGROUND: The National Cancer Institute's Common Terminology Criteria for Adverse Events (NCI-CTCAE) reporting system is widely used by clinicians to measure patient symptoms in clinical trials. The European Organization for Research and Treatment of Cancer's Quality of Life core questionnaire (EORTC QLQ-C30) enables cancer patients to rate their symptoms related to their quality of life. We examined the extent to which patient and clinician symptom scoring and their agreement could contribute to the estimation of overall survival among cancer patients. METHODS: We analyzed baseline data regarding six cancer symptoms (pain, fatigue, vomiting, nausea, diarrhea, and constipation) from a total of 2279 cancer patients from 14 closed EORTC randomized controlled trials. In each trial that was selected for retrospective pooled analysis, both clinician and patient symptom scoring were reported simultaneously at study entry. We assessed the extent of agreement between clinician vs patient symptom scoring using the Spearman and kappa correlation statistics. After adjusting for age, sex, performance status, cancer severity, and cancer site, we used Harrell concordance index (C-index) to compare the potential for clinician-reported and/or patient-reported symptom scores to improve the accuracy of Cox models to predict overall survival. All P values are from two-sided tests. RESULTS: Patient-reported scores for some symptoms, particularly fatigue, did differ from clinician-reported scores. For each of the six symptoms that we assessed at baseline, both clinician and patient scorings contributed independently and positively to the predictive accuracy of survival prognostication. Cox models of overall survival that considered both patient and clinician scores gained more predictive accuracy than models that considered clinician scores alone for each of four symptoms: fatigue (C-index = .67 with both patient and clinician data vs C-index = .63 with clinician data only; P <.001), vomiting (C-index = .64 vs .62; P = .01), nausea (C-index = .65 vs .62; P < .001), and constipation (C-index = .62 vs .61; P = .01). CONCLUSION: Patients provide a subjective measure of symptom severity that complements clinician scoring in predicting overall survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".