How long have we got? The accuracy of physicians’ estimates and scenarios for survival time in 898 women with recurrent ovarian cancer (ROC).
Bibliographic record
Abstract
5549 Background: Predicting, formulating, and communicating prognosis in women with ROC is difficult. Best-case, worst-case, and typical scenarios for survival time based on simple multiples of an individual’s expected survival time (EST) estimated by their oncologist have proven accurate and useful in a range of advanced cancers. We sought the accuracy and prognostic significance of such estimates in the GCIG Symptom Benefit Study: a multinational, prospective cohort study of women with ROC (platinum resistant and potentially platinum sensitive ROC who have had more than 2 lines of chemotherapy). Methods: Oncologists estimated EST at baseline for each woman they recruited to the GCIG Symptom Benefit Study in 11 countries. We hypothesised a priori that oncologists’ estimates of EST would be unbiased (equal proportions [approximately 50%] of women living longer versus shorter than their EST), imprecise ( < 33% living within 0.75 to 1.33 times their EST), and provide accurate scenarios for survival time (approximately 10% dying within ¼ of their EST, 10% living longer than 3 times their EST, and 50% living from half to double their EST). We also hypothesised that oncologists’ estimates of EST would be independently significant predictors of survival in a multivariable Cox model adjusting for prognostic factors established in previous studies. Results: Oncologists’ individualised estimates of EST in 898 women with ROC were unbiased (55% of women lived longer than their EST) and imprecise (23% lived within 0.75 to 1.33 times their EST). Scenarios for survival time based on oncologists’ estimates of EST were remarkably accurate: 7% of women died within ¼ of their EST, 13% lived longer than 3 times their EST, and 53% lived from half to double their EST. The median EST was 12 months (range 3-70), and median observed was 12.7 months. Oncologists’ estimates of EST were independently significant predictors of overall survival (HR 0.96, CI 0.94-0.98, p < 0.0001) in Cox models accounting for previously established prognostic factors. Conclusions: Oncologists’ estimates of EST were unbiased, imprecise, and independently significant predictors of survival time. Best-case, worst-case and typical scenarios based on simple multiples of EST were remarkably accurate, and provide a useful approach for predicting, formulating, and explaining prognosis in women with recurrent ovarian cancer. Clinical trial information: ACTRN: 12607000603415.
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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 | 0.006 | 0.045 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".