Overestimation of Late Distant Recurrences in High-Risk Patients With ER-Positive Breast Cancer: Validity and Accuracy of the CTS5 Risk Score in the TEAM and IDEAL Trials
Bibliographic record
Abstract
PURPOSE Most distant recurrences (DRs) in women with hormone receptor–positive breast cancer occur after 5 years from diagnosis. The Clinical Treatment Score post-5 years (CTS5) estimates DRs after 5 years of adjuvant endocrine therapy (AET). The aim of this study was to externally validate the CTS5 as a prognostic/predictive tool. METHODS The CTS5 categorizes patients who have been disease free for 5 years into low, intermediate, and high risk and calculates an absolute risk for developing DRs between 5 and 10 years. Discrimination and calibration were assessed using data from the TEAM and IDEAL trials. The predictive value of the CTS5 was tested with data from the IDEAL trial. RESULTS A total of 5,895 patients from the TEAM trial and 1,591 patients from the IDEAL trial were included. When assessing the CTS5 discrimination, significantly more DRs were found at 10 years after diagnosis in the CTS5 high- and intermediate-risk groups than in the low-risk group (hazard ratio, 5.7 [95% CI, 3.6 to 8.8] and 2.8 [95% CI, 1.7 to 4.4], respectively). In low- and intermediate-risk patients, the CTS5-predicted DR rates were higher, although not statistically significantly so, than observed rates. However, in high-risk patients, the CTS5-predicted DR rates were significantly higher than observed rates (29% v 19%, respectively; P < .001). The CTS5 was not predictive for extended AET duration. CONCLUSION The CTS5 score as applied to patients treated in the TEAM and IDEAL cohorts discriminates between risk categories but overestimates the risk of late DRs in high-risk patients. Therefore, the numerical risk assessment from the CTS5 calculator in its current form should be interpreted with caution when used in daily clinical practice, particularly in high-risk patients.
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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 | 0.091 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".