Delivering global cancer care: An international study of medical oncology workload.
Bibliographic record
Abstract
e18080 Background: With a disproportionate global burden of cancer, access to care in low-middle income countries (LMICs) is a pressing issue. To our knowledge there is no literature that has described medical oncology (MO) workload in the global context. Here, we report the first results of an international study of global MO training, infrastructure and workload. Methods: A multinational panel of oncologists from diverse practice settings designed a 51 item online survey. The survey was distributed through a snowball method via national oncology societies to chemotherapy-prescribing physicians in 50 countries. Countries were classified into low or low-middle (LMIC), upper-middle (UMIC) and high-income countries (HIC) based on World Bank criteria. Due to small numbers, African nations were reported as a region. The primary objective of this study was to describe the annual number of new cancer patient consults seen per oncologist. Results: 708 physicians completed the survey; 14% (96/708) from LMICs, 21% (152/708) UMICs, and 65% (460/708) HICs. 85% (604/708) of respondents were MOs, 9% (65/708) clinical oncologists, 6% (39/708) other. Respondents worked a median 5 days/week and had 4 weeks of annual paid vacation. The median number of annual consults per oncologist was 175 (IQR 125-375); 16% (114/708) of respondents saw 500+ new patients in a year. Annual case volume in LMICs (median consults 425, 46% respondents seeing > 500 consults) was substantially higher than UMICs (175, 15% > 500) and HICs (175, 10% > 500) (p < 0.001). Among LMICs, UMICs, and HICs, median days worked per week were 6, 5, 5 respectively (p < 0.001); annual weeks of paid vacation were 3, 3, 5 respectively (p < 0.001). Among countries/regions with 10+ responses, the highest annual case volumes per oncologist were Pakistan (median consults 950, 73% > 500 consults), India (475, 47% > 500), Turkey (475, 25% > 500), Africa (400, 42% > 500) and China (325, 31% > 500). Conclusions: There is substantial global variation in oncology case volumes and clinical workload; this is most striking among LMICs. Further work is needed to quantify activity-based global MO practice and workload to inform training needs and the design of new pathways and models of care.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".