Tracking fellowship competitiveness in hematology-oncology: Insights from nrmp match data (2014–2025)
Bibliographic record
Abstract
Abstract Background This study evaluates trends in Hematology-Oncology fellowship match outcomes from 2014 to 2025 using National Resident Matching Program (NRMP) data. The analysis focuses on the distribution of matched applicants among U.S. allopathic graduates (US MDs), osteopathic graduates (DOs), U.S. international medical graduates (US IMGs), and non-U.S. international medical graduates (non-US IMGs). Understanding these trends can inform future applicants, training programs, and policy-makers regarding the evolving competitiveness and accessibility of this subspecialty. Methods NRMP Hematology-Oncology fellowship match data from 2014 to 2025 were obtained from publicly available reports; projected data for 2025 were included when available. Extracted variables included the number of participating programs, positions offered, positions filled, and overall fill rates. Applicant groups analyzed included US MDs, DOs, US IMGs, and non-US IMGs (including Canadian graduates). Key metrics included group-specific fill rates, total match numbers, and outcomes such as first-choice matches and unmatched rates. Trends over time were assessed using simple linear regression, with statistical significance defined as P < 0.05. Results The number of Hematology-Oncology fellowship programs increased by 76.92%, from 130 in 2014 to 230 in 2025 (P < .001), representing an average annual increase of 5.3%. Fellowship positions grew by 49.51%, from 517 to 773 over the same period (P < .001), with an average annual increase of 3.7%, reflecting rising demand and institutional investment. The overall fill rate improved by 2.6%, reaching 99.7% in 2025 compared to 97.1% in 2014, underscoring increasing competitiveness. The proportion of US MDs matched rose from 52.19% in 2014 to 55.38% in 2025. The total number of matched MDs (including US MDs, US IMGs, non-US IMGs and Canadian graduates) increased by 49.05%, from 475 to 708 (P < .001). The proportion of US DOs matched increased from 5.37% (n = 27) in 2014 to 8.2% (n = 63) in 2025 (P < .05), with an average annual growth of 5.62%. The overall proportion of IMGs matched (including US IMGs, non-US IMGs, and Canadians) declined slightly from 42.5% in 2014 to 36.4% in 2025 (P > .05). Within this group, US IMG match rates rose modestly from 9.0% to 9.33% (P > .05), while non-US IMGs (including Canadians) decreased from 33.5% to 27.1% (P < .05). Applicants matching at their first-choice program increased by 45.96%, from 272 in 2014 to 397 in 2025 (P > .05), with an average annual increase of 3.80%. The percentage of applicants who did not match into Hematology-Oncology but matched into another specialty decreased from 6.5% to 3.0% (P > .05). However, the overall unmatched rate rose from 20.6% in 2014 to 25.2% in 2025 (P < .05), indicating a growing level of competition in the specialty. Conclusion Hematology-Oncology has seen substantial growth in both program numbers and match fill rates over the past decade, reflecting increasing interest and clinical demand. The rising match rates among US MDs and DOs point to greater access for domestic graduates, while the relative decline in match rates among non-US IMGs suggests increased competition. These trends likely mirror broader dynamics such as the global rise in cancer incidence, therapeutic innovation, and expanded research opportunities in the field. Continued surveillance of match outcomes may help guide applicant decision-making and inform fellowship program policies, including international recruitment and training capacity planning. References 1.National Resident Matching Program, Results and Data: Specialties Matching Service 2014 Appointment Year. National Resident Matching Program, Washington, DC. 2014. 2.National Resident Matching Program, Results and Data: Specialties Matching Service 2025 Appointment Year. National Resident Matching Program, Washington, DC. 2025
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".