A Population-Based Assessment of Primary Care Visits during Adjuvant Chemotherapy for Breast Cancer
Bibliographic record
Abstract
Background: We used administrative health data to explore the impact of primary care physician (pcp) visits on acute-care service utilization by women receiving adjuvant chemotherapy for early-stage breast cancer (ebc). Methods: Our population-based retrospective cohort study examined pcp visits and acute-care use [defined as an emergency room (er) visit or hospitalization] by women diagnosed with ebc between 2007 and 2009 and treated with adjuvant chemotherapy. Multivariate regression analysis was used to identify the effect of pcp visits on the likelihood of experiencing an acute-care visit. Results: Patients receiving chemotherapy visited a pcp significantly more frequently than they had before their diagnosis [relative risk (rr): 1.48; 95% confidence interval (ci): 1.44 to 1.53; p < 0.001] and significantly more frequently than control subjects without cancer (rr: 1.51; 95% ci: 1.46 to 1.57; p < 0.001). More than one third of pcp visits by chemotherapy patients were related to breast cancer or chemotherapy-related side effects. In adjusted multivariate analyses, the likelihood of experiencing an er visit or hospitalization increased in the days immediately after a pcp visit (rr: 1.92; 95% ci: 1.76 to 2.10; p < 0.001). Conclusions: During chemotherapy treatment, patients visited their pcp more frequently than control subjects did, and they visited for reasons related to their breast cancer or to chemotherapy-related side effects. Visits to a pcp by patients receiving chemotherapy were associated with an increased frequency of er visits or hospitalizations in the days immediately after the pcp visit. Those results suggest an opportunity to institute measures for early detection and intervention in chemotherapy side effects.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".