Impact of the COVID-19 pandemic on primary care access for patients with gastrointestinal malignancies.
Bibliographic record
Abstract
32 Background: Primary care physicians (PCPs) provide essential support for cancer patients. Both primary and cancer care have been affected by the COVID-19 pandemic. In the US, cancer related encounters and screening decreased over 40% and 80% respectively in January to April 2020 compared to 2019 (London et al JCO Clin Cancer Inform 2020). However, the impact of the pandemic on primary care access for cancer patients remains unclear. Methods: This was a population-based, retrospective cohort study using administrative healthcare databases held at ICES in Ontario, Canada. Patients with a new gastrointestinal (GI) malignancy diagnosed within the year prior to the pandemic, between July 1 and Sept 30, 2019 (COVID-19 cohort), were compared to patients diagnosed in years unaffected by the pandemic, between July 1 – Sept 30, 2018 and July 1 – Sept 30, 2017 (pre-pandemic cohort). Both groups were followed for 12 months after initial cancer diagnosis. In the COVID-19 cohort, this allowed for at least 4 months of follow-up data occurring during the pandemic. The primary outcome was number of in-person and telemedicine visits with a PCP. Secondary outcomes were number of in-person and telemedicine visits with a medical oncologist, number of emergency department (ED) visits, and number of unplanned hospitalizations. Outcomes, reported as number of visits per person-year, were compared between the COVID-19 and pre-pandemic cohorts. Results: 2833 individuals diagnosed with a new GI malignancy in the COVID-19 cohort were compared to 5698 individuals in the pre-pandemic cohort. The number of in-person visits to PCPs per person-year significantly decreased from 7.13 [95% CI 7.05 – 7.20] in the pre-pandemic cohort to 4.75 [4.66 – 4.83] in the COVID-19 cohort. Telemedicine visits to PCPs increased from 0.06 [0.05 – 0.07] to 2.07 [2.01 – 2.12]. Combined in-person and telemedicine visits to PCPs decreased from 7.19 [7.11 – 7.26] to 6.82 [6.71 – 6.92]. In-person visits to medical oncologists decreased from 3.73 [3.68 – 3.79] to 2.87 [2.80 – 2.94], and telemedicine visits increased from 0.10 [0.10 – 0.11] to 0.95 [0.91 – 0.99]. Combined in-person and telemedicine visits to medical oncologists remained stable (3.84 [3.78 – 3.89] vs. 3.82 [3.74 – 3.90]). The number of ED visits per person-year decreased from 1.04 [1.01 – 1.07] in the pre-pandemic cohort to 0.93 [0.89 – 0.97] in the COVID-19 cohort. Unplanned hospitalizations did not show a significant change (0.56 [0.54 – 0.58] vs. 0.53 [0.50 – 0.56]). Conclusions: PCP visits for patients with newly diagnosed GI malignancies overall decreased during the pandemic, with a dramatic shift from in-person to telemedicine visits. Visits to medical oncologists also shifted from in-person to telemedicine, but the overall combined visits remained the same. While the number of ED visits decreased, the shift in ambulatory practices did not seem to impact the number of unplanned hospitalizations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".