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 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".