Palliative chemotherapy among people living in poverty with metastasised colon cancer: facilitation by primary care and health insurance
Bibliographic record
Abstract
BACKGROUND: Many Americans with metastasised colon cancer do not receive indicated palliative chemotherapy. We examined the effects of health insurance and physician supplies on such chemotherapy in California. METHODS: We analysed registry data for 1199 people with metastasised colon cancer diagnosed between 1996 and 2000 and followed for 1 year. We obtained data on health insurance, census tract-based socioeconomic status and county-level physician supplies. Poor neighbourhoods were oversampled and the criterion was receipt of chemotherapy. Effects were described with rate ratios (RR) and tested with logistic regression models. RESULTS: Palliative chemotherapy was received by less than half of the participants (45%). Facilitating effects of primary care (RR=1.23) and health insurance (RR=1.14) as well as an impeding effect of specialised care (RR=0.86) were observed. Primary care physician (PCP) supply took precedence. Adjusting for poverty, PCP supply was the only significant and strong predictor of chemotherapy (OR=1.62, 95% CI 1.02 to 2.56). The threshold for this primary care advantage was realised in communities with 8.5 or more PCPs per 10 000 inhabitants. Only 10% of participants lived in such well-supplied communities. CONCLUSIONS: This study's observations of facilitating effects of primary care and health insurance on palliative chemotherapy for metastasised colon cancer clearly suggested a way to maximise Affordable Care Act (ACA) protections. Strengthening America's system of primary care will probably be the best way to ensure that the ACA's full benefits are realised. Such would go a long way towards facilitating access to palliative care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".