External validation of the simple prognostic score in a palliative care clinic at a comprehensive cancer center.
Bibliographic record
Abstract
e19566 Background: Accurate survival predictions are essential for the optimal delivery of palliative care (PC). Clinical predictions of survival are notoriously inaccurate. Tools for making objective survival estimates in ambulatory cancer patients, who have months or years to live, are lacking. The Simple Prognostic Score (SPS) separated Canadian palliative radiotherapy patients into three groups with 12, 6 and 3 months median survivals, respectively. The aim of this study was to test the SPS in an outpatient PC clinic at a U.S. comprehensive cancer center. Methods: Retrospective chart review of 300 consecutive patients referred to one PC clinic at Memorial Sloan-Kettering Cancer Center (MSKCC). Cancer type, metastatic sites and Karnofsky performance status (KPS) score were used to calculate SPS (1 point each for non-breast cancer; non-osseous metastases; KPS score <70%). Outcome was date of death, obtained from the MSKCC institutional data base. Survival analysis was performed. Discrimination (non-overlap between groups) and calibration (percentage difference between estimated and observed survival) were measures of accuracy. Results: 79% (236 of 300) patients had advanced disease. Of them, 90% had cancers other than breast; 76% had metastases in sites other than bone; 27% had a low KPS score. The SPS score was 2 or 3 in 80%. By 10/31/11, 85% had died (median survival 4.9 months). SPS categorized the sample into four prognostic subgroups (see Table), with median survivals of 15, 9, 5 and 2 months respectively (log rank test χ2=38.71, d.f .3, p<0.0001). SPS was not very accurate, with poor discrimination (extensive intergroup overlap) but reasonable calibration (6-15% for the different groups). Conclusions: The SPS is user-friendly and helpful at the group level, but inaccurate at the individual level. Additional variables may narrow the prediction intervals, but will make the tool more complex. [Table: see text]
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".