Validation of a Web-Based Predictive Nomogram for Ipsilateral Breast Tumor Recurrence After Breast Conserving Therapy
Bibliographic record
Abstract
PURPOSE IBTR! version 1.0 is a web-based tool that uses literature-derived relative risk ratios for seven clinicopathologic variables to predict ipsilateral breast tumor recurrence (IBTR) after breast-conserving therapy (BCT). Preliminary testing demonstrated over-estimation in high-risk subgroups. This study uses two independent population-based datasets to create and validate a modified nomogram, IBTR! version 2.0. METHODS Cox regression modeling was performed on 7,811 patients treated with BCT at the British Columbia Cancer Agency (median follow-up, 9.4 years). Population-based hazard ratios were generated for the seven variables in the original nomogram. A modified nomogram was then tested against 664 patients from Massachusetts General Hospital (median follow-up, 9.3 years). The mean predicted and observed 10-year estimates were compared for the entire cohort and for four groups predefined by nomogram-predicted risks: group 1: less than 3%; group 2: 3% to 5%; group 3: 5% to 10%; and group 4: more than 10%. Results IBTR! version 2.0 predicted an overall 10-year IBTR estimate of 4.0% (95% CI, 3.8 to 4.2), while the observed estimate was 2.8% (95% CI, 1.6 to 4.7; P = .10). The predicted and observed IBTR estimates were: group 1 (n = 283): 2.2% versus 1.3%, P = .40; group 2 (n = 237): 3.8% versus 3.5%, P = .80; group 3 (n = 111): 6.7% versus 3.2%, P = .05; and group 4 (n = 33): 12.5% versus 8.7%, P = .50. CONCLUSION IBTR! version 2.0 is accurate in the majority of patients with a low to moderate risk of in-breast recurrence. The nomogram still overestimates risk in a minority of patients with higher risk features. Validation in a larger prospective data set is warranted.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".