Population-Based Validation of the Prognostic Model ADJUVANT! for Early Breast Cancer
Bibliographic record
Abstract
PURPOSE: Adjuvant! (www.adjuvantonline.com) is a web-based tool that predicts 10-year breast cancer outcomes with and without adjuvant systemic therapy, but it has not been independently validated. METHODS: Using the British Columbia Breast Cancer Outcomes Unit (BCOU) database, demographic, pathologic, staging, and treatment data on 4,083 women diagnosed between 1989 and 1993 in British Columbia with T1-2, N0-1, M0 breast cancer were abstracted and entered into Adjuvant! to calculate predicted 10-year overall survival (OS), breast cancer-specific survival (BCSS), and event-free survival (EFS) for each patient. Individual BCOU observed outcomes at 10 years were independently determined. Predicted and observed outcomes were compared. RESULTS: Across all 4,083 patients, 10-year predicted and observed outcomes were within 1% for OS, BCSS, and EFS (all P > .05). Predicted and observed outcomes were within 2% for most demographic, pathologic, and treatment-defined subgroups. Adjuvant! overestimated OS, BCSS, and EFS in women younger than age 35 years (predicted-observed = 8.6%, 9.6%, and 13.6%, respectively; all P < .001) or with lymphatic or vascular invasion (LVI; predicted-observed = 3.6%, 3.8%, and 4.2%, respectively; all P < .05); these two prognostic factors were not automatically incorporated within the Adjuvant! algorithm. After adjusting for the distribution of LVI, using the prognostic factor impact calculator in Adjuvant!, 10-year predicted and observed outcomes were no longer significantly different. CONCLUSION: Adjuvant! performed reliably. Patients younger than age 35 or with known additional adverse prognostic factors such as LVI require adjustment of risks to derive reliable predictions of prognosis without adjuvant systemic therapy and the absolute benefits of adjuvant systemic therapy.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".