Delays in Initiating Anti-Cancer Therapy for Early-Stage Breast Cancer—How Slow Can We Go?
Bibliographic record
Abstract
Breast cancer is the most commonly diagnosed cancer among women worldwide, and is a leading cause of cancer-related deaths. When diagnosed at an early stage, appropriate and timely treatment results in a high cure rate and better quality of life. Delays in initiating anti-cancer therapy, including surgical resection, adjuvant/neoadjuvant chemotherapy and radiation therapy are commonly encountered, even in developed health care systems. Existing comorbidities that mandate referral to other services, genetic counseling and testing that may dictate the extent and type of anti-cancer therapy and insurance coverage, are among the most commonly cited factors. However, delays can be unavoidable; for over three years, health care systems across the globe were busy dealing with the unprecedented COVID-19 pandemic. War across hot zones around the globe resulted in millions of refugees; most of them have no access to cancer care, and when/where available, there may be significant delays. Thus, cancer patients across the globe will probably continue to suffer from significant delays in diagnosis and appropriate treatment. Many retrospective reports showed significant negative impacts on different aspects of treatment outcomes and on patients' psychosocial wellbeing and productivity. In this paper, we review the available data on the impact of delays in initiating appropriate treatment on the outcomes of patients with early-stage breast cancer.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".