Rethinking future workforce planning by developing novel metrics of complexity in cancer care.
Bibliographic record
Abstract
9015 Background: The decision to recruit additional oncologists is often based on simple workload measures such as new patient volumes. This approach may be appropriate previously when cancer management was less complex and at a time when attrition from cancer mortality was significant. We hypothesized that cancer care complexity has increased over time and that new metrics are needed to optimize workforce planning. Methods: We conducted a population-based, retrospective cohort study of adult patients diagnosed with common solid and blood cancers in Alberta, Canada. We focused on cases from 2004 to 2018 to ensure adequate follow-up. We evaluated indicators of complexity including patient characteristics at the time of diagnosis, clinical course within 2 years of diagnosis, and longevity as measured by overall survival (OS). For these complexity indicators, we used logistic and Cox regression models to estimate relative changes over the 15-year study period. Results: A total of 141,040 patients were included in the study cohort, with a median age of 66 years (range 18-107) and 51.7% male. Breast cancer was most common (25.6%), followed by prostate (24.0%), lung (20.2%), colorectal (17.9%) and leukemia/lymphoma (12.4%). Across all sites, annual cancer incidence rate was 249.9 per 100,000 in 2004 and 284.4 per 100,000 in 2018. Age distribution remained largely stable throughout the study period. Meanwhile, specific indicators of complexity increased over time, including polypharmacy at diagnosis (odds ratio [OR] 1.30, 95% confidence interval [CI] 1.28-1.32), multimodality treatment (OR 1.06, 95% CI 1.05-1.08), and hospital admission via the emergency department (OR 1.08, 95% CI 1.07-1.10). These metrics also demonstrated increasing complexity in multivariable analyses after adjusting for age, sex, and cancer site. Similarly, there was a trend towards greater longevity as measured by OS (hazard ratio 0.98, 95% CI 0.98-0.98). Conclusions: Cancer care complexity has increased over time. Workforce planning using antiquated workload metrics, such as incident patient volumes alone, may not align with the actual demands of providing increasingly complex cancer care. Recruitment strategies should consider multi-faceted indicators that reflect complexity in addition to quantity. Trends in metrics of patient complexity, by time era. Characteristic Year of diagnosis 2004-2008, n = 39,465 2009-2013 , n = 46,408 2014-2018 , n = 55,167 Age, y (range) 66 (18, 104) 66 (18, 104) 66 (18, 107) Stage III-IV a 12,706 (41.4%) 15,265 (39.2%) 17,258 (38.1%) Polypharmacy 6,853 (17.4%) 11,175 (24.1%) 14,836 (26.9%) Multimodality treatment 13,884 (35.2%) 17,069 (36.8%) 21,025 (38.1%) Any admission via ED b 7,679 (19.5%) 9,341 (20.1%) 12,057 (21.9%) 2-year OS (95% CI) 0.70 (0.69-0.70) 0.73 (0.73-0.74) 0.76 (0.75-0.76) a solid cancers only; b within 2 years following diagnosis.
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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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".