Social, Prognostic, and Therapeutic Factors Associated with Cancer Survival: A Population-Based Study in Metropolitan Detroit, Michigan
Bibliographic record
Abstract
Previously, we studied the effect of socioeconomic status (SES) on cancer survival among adults of Toronto, Ontario and Detroit, Michigan. 1 Detroiters' survival was significantly worse among people from lower SES areas for 12 of 15 relatively common types of cancer.In contrast, no such SES-survival associations were found for 12 of 15 cancer types in Toronto Between-country analysis, which compared cases arising from Toronto and Detroit's low-income areas, revealed a significant Toronto survival advantage for 13 of 15 most prevalent cancers.Other studies demonstrated that such Canadian advantage was maintained even with a conservative comparison of Toronto's poor with Detroit's near poor, 2 as well as in other Canada-U.S. comparative locales.[3][4][5] Furthermore, SES acted as an effect modifier, that is, significant country-by-SES interactions were observed.Canadian survival advantages were observed only among the ecologically defined poor (residents of lowincome neighborhoods).The present study aims to advance understanding of the factors associated with such disadvantaged survival among people with cancer in the United States.Nine of 10 U.S. studies on cancer survival during the past 10 years have found a significant disadvantage with low SES.1,3,6 Survival among those of relatively high SES was found to be 49 percent greater than that of their lower status counterparts.A similar SES-cancer survival association, although of attenuated magnitude (13 percent differential), has also been observed in other developed continental European and Nordic countries, as well as Australia.[7][8][9][10] Interestingly, the aggregate SES-cancer survival differential among Canadian cohorts has been found to be only 3 percent.[1][2][3][4][5]11,12 Health care systems differences, such as the greater representation of universally accessible single-payer systems in Nordic and other European countries, and Canada, may parsimoniously account for the greatly diminished SES-cancer survival associations found in these countries compared with the United States.Studies of race and cancer survival have provided further evidence for an SES-survival association in the United States.[13][14][15][16] Cumulative cancer survival among blacks was found to be approximately 43 percent less than that of whites, but this difference J Health Care Poor Underserved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".