Natural Language Processing in Breast Cancer Care: A Scoping Review of Clinical and Research Applications
Bibliographic record
Abstract
The aim of this study is to map the current landscape of natural language processing (NLP) applications in breast cancer care and research. Using the PRISMA-ScR framework, we systematically identify and synthesize existing studies that apply NLP techniques, from rule-based models to traditional machine learning (ML) based NLP models like Support Vector Machines (SVM) to transformer-models and deep learning models, to tasks across the entire breast cancer care continuum. This ranges from biomedical knowledge acquisition, classification of initial diagnosis to treatment to recurrence prediction and more. This review will explore trends in NLP purposes, types of clinical text utilized, model types, dataset characteristics, validation strategies and more. By identifying the current literature, we aim to identify methodological gaps and under explored use cases to inform trends surrounding future development, integration and deployment of NLP in both breast cancer clinical and research setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".