The effect of comorbidity on primary care use during breast cancer chemotherapy: a population-based retrospective cohort study using CanIMPACT data
Bibliographic record
Abstract
BACKGROUND: Patients with breast cancer visit their primary care physicians (PCPs) more often during chemotherapy than before diagnosis, but the reasons are unclear. We assessed the association between physical comorbidities and mental health history (MHH) and the change in PCP use during adjuvant breast cancer chemotherapy. METHODS: We conducted a population-based, retrospective cohort study using data from the Canadian Team to Improve Community-Based Cancer Care along the Continuum (CanIMPACT) project. Participants were women 18 years of age and older, who had received a diagnosis of stage I-III breast cancer in Ontario between 2007 and 2011 and had received surgery and adjuvant chemotherapy. We used difference-in-difference analysis using negative binomial modelling to quantify the differences in the 6-month rate of PCP visits at baseline (the 24-month period between 6 and 30 months before diagnosis) and during treatment (the 6 months from start of chemotherapy) between physical comorbidity and MHH groups. RESULTS: Among 12 781 participants, the 6-month PCP visit rate increased during chemotherapy (mean 2.3 visits at baseline, 3.4 visits during chemotherapy). Patients with higher physical comorbidity levels or MHH visited their PCPs 4.2 or 1.7 more times, respectively, over 6 months compared to those with low physical comorbidity or no MHH at baseline and 2.5 or 1.1 more times, respectively, over 6 months during treatment. During treatment, the adjusted 6-month rate of PCP visits more than doubled in the group with the fewest physical comorbidities or no MHH compared with baseline (rate ratio 2.52, 95% confidence interval [CI] 2.43-2.61). This increase was lower in those with MHH (rate ratio 1.81, 95% CI 1.68-1.96) and in the highest physical comorbidity group (rate ratio 1.16, 95% CI 1.07-1.28). INTERPRETATION: Patients with breast cancer who have more physical comorbidities and MHH have a higher frequency of PCP visits during adjuvant chemotherapy but lower absolute and relative increases in visits compared with baseline. Therefore, PCPs can expect to see their patients with fewer physical comorbidities and no MHH more often during chemotherapy. Primary care physicians can plan for their patients with high physical comorbidity levels and MHH to continue having frequent appointments while they undergo chemotherapy, and they can expect their patients with low physical comorbidity levels and no MHH to increase the frequency of their visits during chemotherapy, and should be prepared to provide breast cancer-related care to these patients.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".