Clinicians’ Prediction of Survival Is Most Useful for Palliative Care Referral
Bibliographic record
Abstract
Background: Timely palliative transition in patients with advanced cancer is essential for their improved quality of life and overall survival (OS). Most prognostic models have been developed focusing on weeks' survival. The current study aimed to compare the accuracies of several indicators, such as the Karnofsky Performance Scale (KPS), Clinicians' Prediction of Survival (CPS), and Edmonton Symptom Assessment System (ESAS), for predicting the survival of patients. Methods: Two hundred patients were enrolled at a single tertiary cancer center in South Korea between 2016 and 2019. We compared the discrimination of CPS versus KPS and ESAS total scores using the area under the receiver operating characteristic curve (AUROC) in 3-month and 6-month survival predictions. Results: The median age of patients was 66.0 years, and 128 (64%) were male. Two-thirds (66%) of the patients had an Eastern Cooperative Oncology Group performance status of 0 or 1, and 55.5% had a KPS of 80% or higher. The values of AUROC of CPS, KPS, and ESAS total score in 3-month survival prediction were 0.80 (95% confidence interval [CI]: 0.73-0.88), 0.71 (95% CI: 0.62-0.79), and 0.71 (95% CI: 0.62-0.81), respectively, whereas those in 6-month survival were 0.82 (95% CI: 0.76-0.88), 0.70 (95% CI: 0.63-0.78), and 0.63 (95% CI: 0.55-0.71), respectively. Conclusion: CPS showed the highest accuracy in predicting 3- and 6-month survival, whereas KPS had an acceptable accuracy. Experienced clinicians can rely on CPS to predict survival in months. We recommend the use of KPS with CPS to assist inexperienced clinicians.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".