Recent trends in utilization of outpatient medical oncology services in patients with breast and pancreatic cancer.
Bibliographic record
Abstract
70 Background: Cancer incidence is increasing globally. Effective health and human resource planning will be critical to accommodate the rise in disease burden. Limited data are available on longitudinal utilization of medical oncology services, especially the increasing volume and complexity of care. We sought to characterize trends in medical oncology services following a diagnosis of breast or pancreatic cancers. Methods: We performed a retrospective, population-based analysis of health administrative data from Ontario, Canada to characterize trends of utilization of outpatient medical oncology services following diagnosis in patients diagnosed with breast or pancreatic cancer between 2012 and 2022. The cohort was derived from the Ontario Cancer Registry and stratified by year of diagnosis. The mean number of visits for consultation, follow-up and systemic therapy administration were tabulated per annum following first consultation. Results: Of 139,784 patients, 117, 734 were diagnosed with breast and 22,050 with pancreatic cancer. The proportion of patients with a medical oncologist consultation increased from 2012 to 2022 (breast: 84.1% vs 92.6% [p < 0.001]; pancreatic: 49.1% vs 79% [p < .001]). The proportion of patients who received any systemic therapy increased from 2012 to 2022 (breast: 50.8% vs 76.0% [p < 0.001]; pancreas: 62.2% vs 65.2% [p = 0.13]). The mean number of medical oncologist visits per patient in the first year following initial consultation grew from 2012 to 2022 for both breast (5.6 vs 6.9, p < 0.001) and pancreatic cancers (7.7 vs 10.0, p < 0.001). This trend persisted into the second year of follow-up for both cancers (breast: 2.7 vs 3.1 [p = 0.005]; pancreas: 5.9 vs 7.0 [p < 0.001]). During the same period, the mean number of systemic therapy administrations per patient in the year following initial consultation was 8.2 vs 8.7 in breast (p = 0.03) and 11.9 vs 12.5 in pancreas cancer patients (p = 0.30). Conclusions: Utilization of outpatient medical oncology services following a diagnosis of breast or pancreatic cancer has generally increased over time. Future medical oncology human resource planning needs to take into account the increasing complexity and volume of care delivered.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".