From prospective clinical trial to reducing social inequalities in health: The DESSEIN trial, concept and design of a multidisciplinary study in precarious patients with breast cancer
Bibliographic record
Abstract
BACKGROUND: In France during the last 15 years, precariousness among women has increased. In breast cancer, precariousness has been associated with an increase in mortality, but the links between precariousness, stage at diagnosis and care pathway are little explored. Our study aims to evaluate the impact of precariousness on care pathways, treatment and recovery phase according to a multidisciplinary analysis. METHODS AND DESIGN: Comparative prospective observational multicenter study of exposed / unexposed category. Patients with breast cancer are recruited in the Ile de France area. Three scores are used to identify precarious patients. Precarious patients are matched to non-precarious patients by age group. Questionnaires are distributed to patients at different times of care. The main objective is to compare the stage of the disease at diagnosis between two groups. The secondary objectives are: comparison of socio-economic and geographical characteristics, direct and indirect costs, personal trajectories of care and health. Analysis include multidisciplinary approaches. A geographical information systems method will evaluate the accessibility to health facilities and the characteristics of the places of residence of the patients. An anthropological analysis will be conducted through observation of consultations and semi-directed interviews with patients. These methods will allow to analyze the diagnostic and therapeutic routes, placing it in a life history and an economic, socio-cultural and health environment. The economic analysis will include a comparison of direct, indirect costs and out-off pocket costs, from the patient's point of view and from the societal perspective. DISCUSSION: Conducted in a clinical setting and coupled with a qualitative study, this study will provide a better understanding of how contextual factors, combined with individual factors, can influence the course of health and thus the stage of the disease at diagnosis. The multidisciplinary approach, involving clinicians, geographers, an anthropologist, an economist and a health epidemiologist, will allow a multidimensional approach to the impact of precariousness on breast cancer. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02948478 registered October 28, 2016. ID RCB: 2016-A00589-42. protocol version: 2.1. decembre 13, 2018.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".