The impact of socioeconomic status on overall survival in patients with colorectal cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Colorectal cancer (CRC) is the third most common malignancy and the third leading cause of cancer-related mortality worldwide. Patients are typically diagnosed at an early stage due to widespread use of colonoscopy screenings and stool testing. Despite the efficacy of early detection in CRC, there are concerns about the accessibility of these treatments for patients of lower socioeconomic status (SES), which may lead to poorer outcomes and exacerbate health inequity. Patients and methods We searched Medline and Embase from inception to 26 April 2024 to identify cohort studies comparing SES indicators and CRC outcomes. We computed hazard ratios (HRs) with accompanying 95% confidence intervals (CIs) for each study, and pooled the results using a random-effects meta-analysis. Quality assessment was carried out using Newcastle–Ottawa Quality Assessment Scale for cohort studies. Results In this meta-analysis of 37 studies involving 2 017 509 patients, we analysed the impact of SES on overall survival in patients with CRC. All but three studies were conducted in high-income countries. Our main findings demonstrated that lower income (HR 1.16, 95% CI 1.08-1.23, P < 0.0001), lower educational level (HR 1.24, 95% CI 1.17-1.31, P < 0.0001), lower neighbourhood SES (HR 1.22, 95% CI 1.19-1.25, P < 0.0001), and lower insurance coverage (HR 1.29, 95% CI 1.25-1.32, P < 0.0001) had a negative impact on overall survival in patients with CRC. Conclusion Lower income, educational level, insurance coverage, and neighbourhood SES had a negative impact on overall survival in patients with CRC. There is an urgent need to develop and implement interventions to reduce disparities in outcomes for patients with CRC who are of lower SES.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".