The impact of socioeconomic status on stage of cancer at diagnosis and survival
Bibliographic record
Abstract
BACKGROUND: Lower socioeconomic status (SES) is associated with worsened cancer survival. The authors evaluate the impact of SES on stage of cancer at diagnosis and survival in Ontario, Canada. METHODS: All incident cases of breast, colon, rectal, nonsmall cell lung, cervical, and laryngeal cancer diagnosed in Ontario during the years 2003-2007 were identified by using the Ontario Cancer Registry. Stage information is captured routinely for patients seen at Ontario's 8 Regional Cancer Centers (RCCs). The Ontario population was divided into quintiles (Q1-Q5) based on community median household income reported in the 2001 census; Q1 represents the poorest communities. Overall survival (OS) and cancer-specific survival (CSS) were determined with Kaplan-Meier methodology. A Cox model was used to evaluate the association between survival and SES, stage, and age. RESULTS: Stage at diagnosis was available for 38,431 of 44,802 (85%) of cases seen at RCCs. The authors observed only very small differences in stage distribution by SES. Across all cases in Ontario, the authors found substantial gradients in 5-year OS and 3-year CSS across Q1 and Q5 for breast (7% absolute difference in OS, P < .001; 4% CSS, P < .001), colon (8% OS, P < .001; 3% CSS, P = .002), rectal (9% OS, P < .001; 4% CSS, P = .096), nonsmall cell lung (3% OS, P = .002; 2% CSS, P = .317), cervical (16% OS, P < .001; 10% CSS, P = .118), and laryngeal cancers (1% OS, P = .045; 3% CSS, P = .011). Adjustments for stage and age slightly diminished the survival gradient only among patients with breast cancer. CONCLUSIONS: Despite universal healthcare, SES remains associated with survival among patients with cancer in Ontario, Canada. Disparities in outcome were not explained by differences in stage of cancer at time of diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".