Creation of a new clinical framework – why women choose mastectomy versus breast conserving therapy
Bibliographic record
Abstract
BACKGROUND: Clinical medicine has lagged behind other fields in understanding and utilizing frameworks to guide research. In this article, we introduce a new framework to examine why women choose mastectomy versus breast conserving therapy in early stage breast cancer, and highlight the importance of utilizing a conceptual framework to guide clinical research. METHODS: The framework we present was developed through integrating previous literature, frameworks, theories, models, and the author's past research. RESULTS: We present a conceptual framework that illustrates the central domains that influence women's choice between mastectomy versus breast conserving therapy. These have been organized into three broad constructs: clinicopathological factors, physician factors, and individual factors with subgroups of sociodemographic, geographic, and individual belief factors. The aim of this framework is to provide a comprehensive basis to describe, examine, and explain the factors that influence women's choice of mastectomy versus breast conserving therapy at the individual level. CONCLUSION: We have developed a framework with the purpose of helping health care workers and policy makers better understand the multitude of factors that influence a patient's choice of therapy at an individual level. We hope this framework is useful for future scholars to utilize, challenge, and build upon in their own work on decision-making in the setting of breast cancer. For clinician-researchers who have limited experience with frameworks, this paper will highlight the importance of utilizing a conceptual framework to guide future research and provide an example.
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 | 0.050 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".