Prevention, Screening, and Surveillance Care for Breast Cancer Survivors Compared With Controls: Changes from 1998 to 2002
Bibliographic record
Abstract
PURPOSE: To examine how care for breast cancer survivors compares with controls. PATIENTS AND METHODS: Using the Surveillance, Epidemiology, and End Results-Medicare database, we examined five cohorts of stages 1 to 3 breast cancer survivors diagnosed from 1998 to 2002. For each survivor cohort (defined by diagnosis year), we calculated the number of visits to oncology specialists, primary care providers (PCPs), and other physicians and the percentage who received influenza vaccination, cholesterol screening, colorectal cancer screening, bone densitometry, and mammography during survivorship year 1 (days 366 to 730 postdiagnosis). We compared survivors' care to that of five cohorts of screening controls who were matched to survivors on age, ethnicity, sex, and region and who had a mammogram in the survivor's year of diagnosis and to that of five cohorts of comorbidity controls who were matched on age, ethnicity, sex, region, and comorbidity. We examined whether survivors' care was associated with the mix of physician specialties that were visited. RESULTS: A total of 23,731 survivors were matched with 23,731 screening controls and 23,396 comorbidity controls. There was no difference in trends over time in PCP visits between survivors and either control group. The survivors' rate of increase in other physician visits was greater than screening controls (P = .002) but was no different from comorbidity controls. Survivors were less likely to receive preventive care than screening controls but were more likely than comorbidity controls. Trends over time in survivors' care tended to be better than screening controls but were no different than comorbidity controls. Survivors who visited both a PCP and oncology specialist were most likely to receive recommended care. CONCLUSION: Involvement by both PCPs and oncology specialists can facilitate appropriate care for survivors.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".