The impact of working conditions on breast cancer outcomes: a study protocol for a population-based cohort study using UK Biobank data
Bibliographic record
Abstract
INTRODUCTION: Breast cancer is the most common cancer among women globally. While the impact of lifestyle factors like smoking and obesity on breast cancer risk and survival is well documented, the effect of working conditions is not fully understood. Moreover, breast cancer can reduce employability, making it crucial to identify factors that facilitate return to work and improve life satisfaction. Since breast cancer is affected by sleep and lifestyle, which are related to working conditions, understanding how they affect breast cancer outcomes is key. This study aims to explore the relationship between working conditions and breast cancer outcomes, including incidence, mortality and survival within a causal framework. Our specific aims are to understand the relationship between (1) working conditions and occupational groups and breast cancer outcomes, including the extent to which sleep, lifestyle and breast cancer screening uptake explain these relationships and (2) prediagnosis working conditions, sleep and lifestyle and their effect on return to work and life satisfaction among breast cancer survivors. METHODS AND ANALYSIS: We will use data from the UK Biobank, a large-scale cohort study with data on 273 825 women between 40 and 69 years old at baseline, followed from 2006 to 2022. The data has been linked with death and cancer registries and includes 8309 incident breast cancer cases. To quantify the effect of working conditions on breast cancer outcomes (aim 1) and their effect on return to work and life satisfaction (aim 2), we will implement g-methods to estimate the average causal effect and employ counterfactual-based mediation analysis to quantify how much mediating factors, such as sleep and lifestyle, explain this effect. ETHICS AND DISSEMINATION: UK Biobank received ethical approval from the North West Multi-Centre Research Ethics Committee. No further ethical approval was required for the proposed research project. In line with the two aims, four original research manuscripts will be published in open-access peer-reviewed journals to disseminate the findings. In addition, findings will be disseminated at international conferences and scientific meetings.
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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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 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".