Evaluating PREDICT and developing outcome prediction models in early-onset breast cancer using data from Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Outcome prediction research in early-onset breast cancer (EoBC) is limited. This study evaluated the predictive performance of NHS PREDICT v2.1 and developed two prediction models for 5-year and 10-year all-cause mortality in a cohort of EoBC patients in Alberta, Canada. METHODS: Adults < 40 years diagnosed with invasive breast cancer in Alberta, Canada from 2004 to 2020 were included. Patient data were entered into PREDICT v2.1 and mortality estimates at 5 and 10 years were extracted. Two prediction models were developed for all-cause mortality: multivariable Cox regression with LASSO penalization (LASSO Cox) and random survival forests (RSF). Internal validation of the developed models was performed using nested tenfold cross-validation repeated 200 times. Model performance was assessed using receiver operator characteristic and calibration curves for mortality at 5 and 10 years. RESULTS: In total, 1827 patients with EoBC were eligible for inclusion. At 5 years, PREDICT had an area under the curve of 0.78 (95%CI 0.74-0.82) and overestimated mortality by 2.4% (95%CI 0.70-4.33) in the overall cohort. No differences in observed and predicted mortality by PREDICT were observed at 10 years. The LASSO Cox model showed better discrimination at 5 and 10 years than the RSF model, but both had poor calibration and underestimated mortality. CONCLUSION: PREDICT v2.1 tended to overestimate 5-year mortality in those with > 30% predicted risks and 10-year mortality in those with > 50% predicted risks for EoBC in Alberta, Canada. We did not identify additional models that would be clinically useful by applying machine learning. More follow-up data and emerging systemic treatment variables are required to study outcome prediction in modern cohorts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".