Variations in time to breast cancer treatment initiation and survival across ethnoracial groups: a DAG-based protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Racial disparities in access to care are a known driver of differential health outcomes. There is a need to synthesize knowledge on the impact and mechanisms of ethnoracial variations in initiation of breast cancer treatment. The aim of the study described in this protocol is to systematically review the existing evidence on ethnoracial disparities in initiation of breast cancer treatments and their impact on patients’ survival in the United States.Methods: A comprehensive systematic search of databases including PubMed, Ovid, Web of science, and the Cochran library will be performed. An extra search filtered on the title will be carried out on Google Scholar. Two main keywords, ‘breast cancer’ and ‘time to treatment,’ will be used in search strings. The review process will follow Preferred Reporting Items for Systematic Review and the Meta-Analysis Protocols (PRISMA-P) guidelines and will include studies of cohorts of female breast cancer patients who were diagnosed with stage I-III in the US. The Risk Of Bias In Non-randomised Studies – of Interventions (ROBINS-I) tool will be applied for bias assessment, and only studies with low or moderate risk of bias will be included. A modified checklist for applying the Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs) method is developed to describe the causal relationships between ethnoracial group membership, other study variables, delays to treatment, and survival. Discussion: This systematic review will summarize the impact of racial disparity on breast cancer survival considering delays in receiving treatments as a mediator. Future directions will be identified to address existing gaps potentially relevant to creating equity for racial and ethnic minority groups. The results can help health authorities to develop approaches for addressing racial disparities in access to breast cancer treatment at the population level.
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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 | 0.107 | 0.182 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.009 |
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".