Including the Ductal Carcinoma-In-Situ (DCIS) Score in the Development of a Multivariable Prediction Model for Recurrence After Excision of DCIS
Bibliographic record
Abstract
INTRODUCTION: Individual prediction of local recurrence (LR) risk after breast-conserving surgery (BCS) for ductal carcinoma-in-situ (DCIS) is needed to identify women at low risk, for whom radiotherapy may be omitted. PATIENTS AND METHODS: Three predictive models of LR-clinicopathologic factors (CPF) alone; CPF + estrogen receptor (ER) + human epidermal growth factor receptor 2 (HER2); and CPF + DCIS score (DS)-were developed among 1102 cases of DCIS in patients with complete covariate and outcome data. Categorizations of discrete variables and transformations of continuous variables were examined in Cox models; 2-way interactions and interactions with time were assessed. Internal validation was performed by bootstrapping. Individual predicted 10-year LR risks were computed from covariate values, estimated regression parameters, and estimated baseline survival function. Accuracy was assessed by c statistics and calibration plots. RESULTS: The strongest prediction model incorporated CPF + DS. The c statistics for CPF + DS, CPF + ER + HER2, or CPF-alone models were 0.7025, 0.6879, and 0.6825, respectively. The CPF + DS model was better calibrated at predicting low (≤ 10%) individual 10-year LR risks after BCS alone than those incorporating CPF + ER + HER2 or CPF alone, evidenced by c statistics and plots of observed by predicted risks. Among women aged ≥ 50 with no adverse CPF, the CPF + DS model identified the greatest proportion of women (62.3%) with predicted individual 10-year LR ≤ 10% without radiotherapy compared to the CPF + ER + HER2 (50.9%) or CPF alone (46.5%) models. CONCLUSION: Individual prediction of LR incorporating DS is more accurate and identifies a higher proportion of women with low predicted risk of LR after BCS alone, for whom radiotherapy may be omitted.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".