PREDICT underestimates survival of patients with HER2-positive early-stage breast cancer
Bibliographic record
Abstract
The prognostic performance of PREDICT in patients with HER2-positive early breast cancer (EBC) treated in the modern era with effective chemotherapy and anti-HER2 targeted therapies is unclear. Therefore, we investigated its prognostic performance using data extracted from ALTTO, a phase III trial evaluating adjuvant lapatinib ± trastuzumab vs. trastuzumab alone in patients with HER2-positive EBC. Our analysis included 2794 patients. After a median follow-up of 6.0 years (IQR, 5.8-6.7), 182 deaths were observed. Overall, PREDICT underestimated 5-year OS by 6.7% (95% CI, 5.8-7.6): observed 5-year OS was 94.7% vs. predicted 88.0%. The underestimation was consistent across all subgroups, including those according to the type of anti HER2-therapy. The highest absolute differences were observed for patients with hormone receptor negative-disease, nodal involvement, and large tumor size (13.0%, 15.8%, and 15.3%, respectively). AUC under the ROC curve was 73.7% (95% CI 69.7-77.8) in the overall population, ranging between 61.7% and 77.7% across the analyzed subgroups. In conclusion, our analysis showed that PREDICT highly underestimated OS in HER2-positive EBC. Hence, it should be used with caution to give prognostic estimation to HER2-positive EBC patients treated in the modern era with effective chemotherapy and anti-HER2 targeted therapies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".