The Impact of Primary Care Physicians on Follow-up Care of Underserved Breast Cancer Survivors
Bibliographic record
Abstract
PURPOSE: To investigate the impact of the involvement of primary care physicians (PCPs) on the receipt of preventive follow-up care after a breast cancer (BC) diagnosis among a low-income population. METHODS: Multiple logistic regression analyses were performed to identify potential factors associated with receipt of preventive care among 579 low-income women with BC. The main outcome variables at 36 months after BC diagnosis were receipt of annual mammography, Papanicolaou smear in the past 2 years, and ever had colonoscopy for those who were at least 50 years old. The main independent variable was type of provider visit in the past 12 months. RESULTS: Women with a PCP visit only or both PCP and surgeon/cancer specialist visits in the past 12 months were more likely to have had annual mammography (adjusted odds ratio [AOR], 2.67; P = .109 and AOR, 2.20, P = .0008, respectively), a Papanicolaou smear in the past 2 years (AOR, 2.90; P = .04 and AOR, 2.24, P = .009, respectively), and colonoscopy (AOR, 2.99; P = .041 and AOR, 2.17; P = .026, respectively) than those who only visited surgeons/cancer specialists. Indeed, women who saw only a PCP for their follow-up care had the highest odds ratio of receiving each clinical care service. CONCLUSIONS: The involvement of PCPs in the medical care of low-income BC survivors results in better preventive follow-up care. Getting PCPs involved in the care of cancer survivors might be particularly pertinent for low-income populations because of lower costs and ease of access compared with cancer specialist-provided care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".