The association between visiting a primary care provider and uptake of periodic mammograms as women get older
Bibliographic record
Abstract
OBJECTIVE: To determine whether visits to a primary care provider (PCP) are associated with the uptake of periodic mammograms as women get older. METHODS: The cohort consisted of 2,389,889 women resident in Ontario, Canada, aged 50 to 79 at any point from 2001 to 2010, who were cancer-free and eligible for the Ontario Health Insurance Plan prior to study entry. Non-parametric estimation was used to describe the mean cumulative number of periodic mammograms for women with and without recent exposure to a PCP, as a function of age. Using age as the time scale, a recurrent event regression model was also implemented to examine the association between exposure to a PCP and rate of periodic mammograms, adjusted for income quintile and comorbidity. RESULTS: The mean observation window was 7.0 years. Uptake of periodic mammograms was significantly higher for women with recent exposure to a PCP compared with those without. This trend remained consistent as women aged, and the magnitude of the association increased for women aged 65 or older. The relative rate of periodic mammograms was lower than 1 and consistently decreased as women from lower income quintiles were compared with women from the wealthiest quintile. CONCLUSION: Visits to a PCP play an important role in uptake of periodic mammograms, and this association increases as women age.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".