Health outcome and economic measurement in breast cancer surgery: challenges and opportunities
Bibliographic record
Abstract
As breast cancer surgery techniques continue to advance, treatment options continue to increase, bringing with them increased scrutiny of health outcomes and healthcare cost. In addition, patients are becoming more involved in their own medical care and are demanding meaningful data to help them better understand expected outcomes. With these changes and advancements, there is a growing emphasis on evidence-based practice. In this article, we focus on scientific considerations, challenges to and opportunities for improving outcome measurement related to breast cancer surgery. There are two main messages from this article. First, until recently, rigorously developed specific patient-reported outcome (PRO) measures for breast cancer surgery patients have not been available for use. However, with the recent introduction of new PRO measures, such as the BREAST-Q, there is now good potential to collect useful outcome data on patient satisfaction and health-related quality of life, and to better understand the relative impact of different surgical procedures, decision making and clinical practice on patient outcome. Thus, PRO research using rigorously developed breast cancer surgery-specific measures is in its infancy, but growing steadily. Second, there is a great need but lack of specific health economic measures developed for use in breast cancer surgery research. In fact, research into the economic evaluation of breast cancer surgery is an area that has received less attention than that of PRO measure development, but there is good opportunity to expand this area of research in breast cancer surgery. Further studies are required to gain a clearer view of the role that generic preference and utility measures could play, how best to synthesize health-related quality of life and economic metrics data, and the potential use of new disease-specific tools.
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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 | 0.265 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| 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".