How do age and social environment affect the dynamics of death hazard and survival in patients with breast or gynecological cancer in France?
Bibliographic record
Abstract
Several studies have investigated the association between net survival (NS) and social inequalities in people with cancer, highlighting a varying influence of deprivation depending on the type of cancer studied. However, few of these studies have accounted for the effect of social inequalities over the follow-up period, and/or according to the age of the patients. Thus, using recent and more relevant statistical models, we investigated the effect of social environment on NS in women with breast or gynecological cancer in France. The data were derived from population-based cancer registries, and women diagnosed with breast or gynecological cancer between 2006 and 2009 were included. We used the European deprivation index (EDI), an aggregated index, to define the social environment of the women included. Multidimensional penalized splines were used to model excess mortality hazard. We observed a significant effect of the EDI on NS in women with breast cancer throughout the follow-up period, and especially at 1.5 years of follow-up in women with cervical cancer. Regarding corpus uteri and ovarian cancer patients, the effect of deprivation on NS was less pronounced. These results highlight the impact of social environment on NS in women with breast or gynecological cancer in France thanks to a relevant statistical approach, and identify the follow-up periods during which the social environment may have a particular influence. These findings could help investigate targeted actions for each cancer type, particularly in the most deprived areas, at the time of diagnosis and during follow-up.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".