Predictive survival ability of patient-reported in comparison to physician-reported performance status in solid malignancies.
Bibliographic record
Abstract
7019 Background: Performance status (PS) is an important prognostic tool in cancer management that is mainly generated by physicians. In oncology, the Eastern Cooperative Oncology Group (ECOG) measure is commonly used. Patient-reported functional status (PRFS) is an emerging method that allows patients to provide an estimate of their function; however, there is limited information about its prognostic significance in solid tumors. We explored the prognostic value of PRFS in comparison to ECOG on survival. Methods: 13,045 newly diagnosed cancer patients in Ontario, Canada, who had information from both PRFS and ECOG on the same day of an outpatient visit between March 2013 and March 2018 were included. The dataset were randomly divided into 60% training (n = 7,827) and 40% validation (n = 5,218) cohorts. Covariates were similar at baseline for both training and validation datasets. Survival was estimated by modeling clinical characteristics with PRFS, with ECOG, and alone. Results: PRFS and ECOG scores were statistically significant predictors of overall survival. Both higher PRFS and ECOG scores tended to be associated with inferior survival, hazard ratio (HR) = 1.71 (P < .0001), and HR = 1.90 (P < .0001) respectively. Models that included either PRFS or ECOG scores outperformed the model with baseline clinical characteristics only. C statistics were 0.836, 0.839, and 0.811 respectively. Conclusions: Patient-reported functional status adds to survival modeling and is equally predictive as the ECOG scale at various stages of solid malignancies. PRFS may be used instead of ECOG in clinical or research setting for survival estimation.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".